Method, device, equipment, medium and program product for constructing decision tree

Abstract

The application discloses a decision tree construction method, device, equipment, medium and program product. The method comprises the following steps: obtaining first historical use information generated when a target communication identifier uses different service types within a first preset time period in a database; calculating the proportional relationship between the first historical use information to obtain first historical cross information; generating information of an initial decision tree based on the first historical use information and the first historical cross information, including an initial node and corresponding initial quantity and initial weight; adjusting the initial quantity and the initial weight to obtain information of a first decision tree, the information of the first decision tree including a first node and corresponding first quantity and first weight, and a first loss value corresponding to the first decision tree being less than a preset loss threshold, the first loss value being positively correlated with the first quantity and the first weight respectively. According to the embodiment of the application, the classification accuracy of the decision tree can be improved, so that the type of the communication identifier can be accurately identified.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a decision tree construction method, apparatus, device, medium, and program product. Background Art

[0002] In the current communication environment, fraud is becoming increasingly rampant, causing serious property losses to the majority of users.

[0003] Existing anti-fraud technologies typically focus solely on basic communication behaviors, such as call duration and number of text messages, and then generate blacklists or whitelists based on these behaviors. These blacklists are then used to analyze communication identification types. Because existing technologies rely solely on simple data features and analysis rules, they have limitations, resulting in low classification accuracy.

[0004] Therefore, the existing technology has the problem of low accuracy when identifying the type of communication identification. Summary of the Invention

[0005] The embodiments of the present application provide a decision tree construction method, apparatus, device, medium and program product, which can solve the problem of low accuracy in identifying the type of communication identifier in related technologies.

[0006] In a first aspect, an embodiment of the present application provides a method for constructing a decision tree, wherein the decision tree is used to determine a communication identification type, the method comprising:

[0007] Acquiring, from a database, first historical usage information generated when a target communication identifier uses different service types within a first preset time period;

[0008] Calculating the proportional relationship between the first historical usage information to obtain first historical cross information;

[0009] Generate information of an initial decision tree based on the first historical usage information and the first historical cross information, wherein the information of the initial decision tree includes initial nodes and their corresponding initial numbers and initial weights;

[0010] Adjust the initial quantity and initial weight to obtain information of the first decision tree, wherein the information of the first decision tree includes the first node and its corresponding first quantity and first weight, and the first loss value corresponding to the first decision tree is less than the preset loss threshold, and the first loss value is positively correlated with the first quantity and the first weight, respectively.

[0011] In a possible embodiment of the first aspect, obtaining, in a database, first historical usage information generated when a target communication identifier uses different service types includes:

[0012] Obtaining, from a database, first historical call information generated when the target communication identifier uses a call service, first historical text message information generated when the target communication identifier uses a text message service, and first historical traffic information generated when the target communication identifier uses a traffic service;

[0013] Calculate the proportional relationship between the first historical usage information to obtain the first historical cross information, including:

[0014] The first historical cross information is determined according to a proportional relationship among the first historical call information, the first historical text message information, and the first historical traffic information.

[0015] In a possible embodiment of the first aspect, the first historical call information includes the number of first calls; the first historical text message information includes the number of first text messages; the first historical traffic information includes the amount of application usage; the first historical cross-information includes first sub-historical cross-information, second sub-historical cross-information, and third sub-historical cross-information; and determining the first historical cross-information based on a proportional relationship among the first historical call information, the first historical text message information, and the first historical traffic information includes:

[0016] Divide the first number of call items by the first number of text messages to obtain first sub-history cross information;

[0017] Divide the first call count by the number of application usages to obtain second sub-history cross information;

[0018] The third sub-history cross information is obtained by dividing the first number of short messages by the number of application usages.

[0019] In a possible embodiment of the first aspect, the first historical call information further includes at least one of the following: number of active call days, call frequency, call duration, call device identifier, number of call locations, number of call contact users, proportion of call times, proportion of calling users, proportion of call duration intervals, and regularity information of call time points;

[0020] The first historical SMS information also includes at least one of the following: number of active SMS days, SMS frequency, number of users contacted by SMS, average number of users contacted by SMS per day, SMS type, and regularity of SMS timing;

[0021] The first historical traffic information also includes at least one of the number of active traffic days and the total traffic.

[0022] In a possible embodiment of the first aspect, generating information of an initial decision tree based on the first historical usage information and the first historical cross information includes:

[0023] Adding the amount of the first historical usage information and the amount of the first historical cross information to obtain an initial number of initial nodes in the initial decision tree, wherein the initial nodes correspond one-to-one to the first historical usage information and the first historical cross information;

[0024] From the correspondence between preset historical usage information, historical cross information and preset weights, the target preset weights corresponding to the first historical usage information and the first historical cross information are obtained, and the target preset weights are used as the initial weights of the corresponding initial nodes.

[0025] In a possible embodiment of the first aspect, the first loss value is positively correlated with the first quantity and the first weight, respectively, including:

[0026]

[0027] Where LOSS is the first loss value, a and b are preset constants, N is the first number of the first node, W i is the first weight of the first node.

[0028] In a possible embodiment of the first aspect, the further comprising:

[0029] Acquire, from the database, second historical usage information generated when the target communication identifier uses different service types within a second preset time period and a historical real type to which the target communication identifier belongs;

[0030] Calculating the proportional relationship between the second historical usage information to obtain the second historical cross information;

[0031] inputting the second historical usage information and the second historical cross information into corresponding first nodes in the first decision tree respectively to obtain a plurality of historical sub-results;

[0032] Multiply each historical sub-result by the corresponding first weight and add them together to obtain the historical result;

[0033] Compare the historical results with the historical true types to obtain the error value;

[0034] When the error value is greater than the preset error threshold, the first quantity and the first weight are adjusted to obtain information of the second decision tree, wherein the information of the second decision tree includes the second node and its corresponding second quantity and second weight, and the second loss value corresponding to the second decision tree is less than the preset loss threshold, and the second loss value is positively correlated with the second quantity and the second weight, respectively.

[0035] In a possible embodiment of the first aspect, the further comprising:

[0036] Acquiring, from a database, third historical usage information generated when the target communication identifier uses different service types within a third preset time period;

[0037] Calculating the proportional relationship between the third historical usage information to obtain the third historical cross information;

[0038] inputting the third historical usage information and the third historical cross information into corresponding second nodes in the second decision tree respectively to obtain a plurality of sub-results;

[0039] Multiply each of the multiple sub-results by the corresponding second weight and add them together to obtain the target result.

[0040] Based on the same inventive concept, in a second aspect, an embodiment of the present application further provides a device for constructing a decision tree, the decision tree being used to determine a communication identification type, the device comprising:

[0041] An acquisition module, configured to acquire, from a database, first historical usage information generated when a target communication identifier uses different service types within a first preset time period;

[0042] a calculation module, configured to calculate a proportional relationship between the first historical usage information to obtain first historical cross information;

[0043] a generating module, configured to generate information of an initial decision tree based on the first historical usage information and the first historical cross information, wherein the information of the initial decision tree includes an initial number of initial nodes and an initial weight of each initial node;

[0044] An adjustment module is used to adjust the initial quantity and initial weight to obtain information of the first decision tree, wherein the information of the first decision tree includes the first node and its corresponding first quantity and first weight, and the first loss value corresponding to the first decision tree is less than a preset loss threshold, and the first loss value is positively correlated with the first quantity and the first weight, respectively.

[0045] Based on the same inventive concept, in a third aspect, an embodiment of the present application further provides a decision tree construction device, which includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the decision tree construction method in the first aspect, or any embodiment of the first aspect.

[0046] Based on the same inventive concept, in a fourth aspect, an embodiment of the present application further provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method for constructing a decision tree in the first aspect or any embodiment of the first aspect is implemented.

[0047] Based on the same inventive concept, in the fifth aspect, an embodiment of the present application further provides a computer program product. When the instructions in the computer program product are executed by the processor of the device, the device is able to execute the decision tree construction method in the first aspect or any embodiment of the first aspect.

[0048] The decision tree construction method, apparatus, device, medium, and program product of the embodiments of the present application obtain detailed first historical usage information generated by the target communication identifier when using various services within a first preset time period from a database, and then accurately calculate the proportional relationship between this information to obtain first historical cross-information. The first historical cross-information reflects the inherent connection between different communication behaviors, helps to more accurately describe the user's communication behavior characteristics, and provides a rich data foundation for the construction of the decision tree. Then, based on the first historical usage information and the first historical cross-information, information of the initial decision tree can be generated, such as information on the initial nodes in the initial decision tree, the initial number of initial nodes, and the initial weights of the initial nodes. Then, by carefully adjusting the initial number and initial weights of the initial nodes, the decision tree structure is continuously optimized until the loss value corresponding to the decision tree is lower than the preset loss threshold, that is, the first loss value corresponding to the generated first decision tree is lower than the preset loss threshold, and since the first loss value is positively correlated with the first number of first nodes in the first decision tree and the first weight of the first node, it can avoid the overfitting problem caused by excessive complexity (too many nodes) and the underfitting problem caused by oversimplification (too few nodes and too concentrated weights), thereby finding the best balance point, improving the classification accuracy of the decision tree, and accurately identifying the type of communication identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Other features, objects and advantages of the present application will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings, in which the same or similar reference numerals represent the same or similar features and the accompanying drawings are not drawn to scale.

[0050] Figure 1 This is a flow chart of a method for constructing a decision tree provided in an embodiment of the present application;

[0051] Figure 2 This is another flow chart of the method for constructing a decision tree provided in an embodiment of the present application;

[0052] Figure 3 This is another flowchart of the decision tree construction method provided in the embodiment of the present application;

[0053] Figure 4 This is another flow chart of the method for constructing a decision tree provided in an embodiment of the present application;

[0054] Figure 5 This is a flow chart of optimizing the first decision tree in the decision tree construction method provided in an embodiment of the present application;

[0055] Figure 6 This is a flow chart of using a second decision tree to determine a communication identification type in the decision tree construction method provided in an embodiment of the present application;

[0056] Figure 7 This is a schematic diagram of the structure of a decision tree construction device provided in an embodiment of the present application;

[0057] Figure 8 This is a structural diagram of a decision tree construction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present application and are not configured to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0059] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0060] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0061] It will be apparent to those skilled in the art that various modifications and variations can be made in this application without departing from the spirit or scope of this application. Therefore, this application is intended to cover modifications and variations of this application that fall within the scope of the corresponding claims (technical solutions claimed for protection) and their equivalents. It should be noted that the embodiments provided in the examples of this application can be combined with each other without contradiction.

[0062] Before describing the technical solutions provided by the embodiments of the present application, in order to facilitate understanding of the embodiments of the present application, the present application first specifically describes the problems existing in the related art:

[0063] Related anti-fraud technologies typically focus solely on basic communication behaviors, such as call duration and number of text messages, while ignoring the complex relationships between these behaviors. These technologies then generate blacklists or whitelists based on these behaviors and rely on these blacklists to analyze communication identification types. This reliance on simple data features and analysis rules has limitations, resulting in low classification accuracy.

[0064] Therefore, the related technology has the problem of low accuracy when identifying the type of communication identification.

[0065] Based on this, the embodiments of the present application provide a decision tree construction method, apparatus, equipment, medium and program product, which can create a decision tree for identifying the type of communication identification based on basic communication behaviors and the intrinsic connection between different communication behaviors. Since the loss value of the first decision tree created is positively correlated with the number of nodes, the overfitting problem caused by too many nodes can be avoided. At the same time, since the loss value of the first decision tree created is positively correlated with the weight of the node, the underfitting problem caused by overly concentrated weight distribution is further prevented, thereby finding the optimal balance point, improving the classification accuracy of the decision tree, and accurately identifying the type of communication identification.

[0066] The following describes in detail the method for constructing a decision tree provided in the embodiments of the present application with reference to the accompanying drawings.

[0067] Figure 1 This is a flow chart of a method for constructing a decision tree provided in an embodiment of the present application. The decision tree is used to determine the type of communication identification, such as Figure 1 As shown, the method may include steps S110 to S140.

[0068] S110 , obtaining, from a database, first historical usage information generated when a target communication identifier uses different service types within a first preset time period.

[0069] Among them, the communication identifier is a unique identifier used by the user for communication, such as a telephone number, a user account identifier of a communication software, etc.

[0070] The target communication identifier is a communication identifier whose true type of communication identifier within the first preset time period is known, and is mainly used for training a decision tree.

[0071] A decision tree can determine whether a target communication identifier is normal or abnormal. A decision tree consists of nodes (including a root node, internal nodes, and leaf nodes) and branches, describing a series of possible decision paths and their outcomes. Each internal node in a decision tree represents a characteristic attribute or judgment condition.

[0072] The first preset period is a pre-set time period. The type of communication identification can be determined based on the data within this time period. The first preset period can be set according to demand, and the embodiment of the present application does not limit this, such as one month, one quarter, etc.

[0073] Service types are the different types of services or products provided. In a communications system, service types can include voice calls, text messages, and data services. Different service types have different billing methods or user behavior patterns.

[0074] The first historical usage information is data records generated when the target communication identifier uses different service types within a first preset period of time, such as data records generated when using voice call services, data records generated when using SMS services, and data records generated when using data services.

[0075] Specifically, historical usage information of the target communication identifier using different service types (such as voice calls, text messages, traffic services, etc.) within a first preset time period is obtained in the database. The historical usage information can be referred to as first historical usage information.

[0076] S120: Calculate the proportional relationship between the first historical usage information to obtain first historical cross information.

[0077] Specifically, by analyzing the relative size or ratio of historical usage information between different service types, we can generate new data reflecting the correlation between these historical usage information. This new data can be referred to as the first historical cross-information. By mining voice call, SMS, and data usage information to construct user behavior profiles, we can identify fraudulent numbers. Furthermore, we further explore the pairwise cross-characteristics of calls, SMS, and data usage, revealing the behavioral characteristics of fraudulent numbers from multiple dimensions.

[0078] S130 , generating information of an initial decision tree based on the first historical usage information and the first historical cross information, wherein the information of the initial decision tree includes initial nodes and their corresponding initial numbers and initial weights.

[0079] The information of the initial decision tree may include nodes within the initial decision tree, which may be referred to as initial nodes; the total number of initial nodes, which may be referred to as initial number; and the weight corresponding to each initial node, which may be referred to as initial weight.

[0080] The initial weight can be used to evaluate the importance of the initial node. For example, it can be obtained by supervised training based on the actual types (such as normal types or abnormal types) of the communication identifiers of multiple different target communication identifiers within the first preset time period.

[0081] Specifically, based on the first historical usage information and the first historical cross information, an initial decision tree may be constructed, thereby generating information of the initial decision tree, including initial nodes and their corresponding initial numbers and initial weights.

[0082] S140, adjust the initial quantity and initial weight to obtain information of the first decision tree, wherein the information of the first decision tree includes the first node and its corresponding first quantity and first weight, and the first loss value corresponding to the first decision tree is less than the preset loss threshold, and the first loss value is positively correlated with the first quantity and the first weight, respectively.

[0083] The preset loss threshold is a pre-set value used to determine whether the performance of the decision tree meets the requirements. If the first loss value is less than the preset loss threshold, the performance of the first decision tree is qualified.

[0084] The first loss value is positively correlated with the first number and the first weight, respectively. That is, as the first number or the first weight increases, the first loss value also increases. When the first loss value is less than the preset loss threshold, it means that a balance point has been found, making the decision tree neither too simple (leading to underfitting) nor too complex (leading to overfitting).

[0085] Specifically, by adjusting the total number of nodes and node weights during the decision tree construction process, the loss value of the decision tree can be made lower than the preset loss threshold. Since the loss value is positively correlated with the total number of nodes, it can prevent the decision tree from having too many nodes and being too complex, thus avoiding overfitting. At the same time, since the loss value is positively correlated with the node weights, it can prevent the number of nodes from being too few and the weights from being too concentrated, thus avoiding underfitting.

[0086] It's important to note that when adjusting the number of nodes, or in other words, when performing feature selection, you can consider the divergence of features and their correlation with the target variable. Feature divergence refers to the variability of samples on that feature, typically measured by whether the variance is close to zero. If a feature doesn't diverge, it's assumed to contribute nothing to sample differentiation and can generally be deleted. Regarding the correlation between a feature and the target variable, specific evaluation metrics can be used to determine whether they are correlated and, therefore, whether the feature should be deleted.

[0087] According to the decision tree construction method provided in the embodiments of the present application, by obtaining from a database detailed first historical usage information generated when the target communication identifier uses various services within a first preset time period, the proportional relationship between this information can be precisely calculated to obtain first historical cross-information. This first historical cross-information reflects the inherent connections between different communication behaviors, helps to more accurately describe the user's communication behavior characteristics, and provides a rich data foundation for the construction of the decision tree. Then, based on the first historical usage information and the first historical cross-information, information about the initial decision tree can be generated, such as information about the initial nodes, the initial number of initial nodes, and the initial weights of the initial nodes within the initial decision tree. Then, by carefully adjusting the initial number and initial weights of the initial nodes, the decision tree structure is continuously optimized until the loss value corresponding to the decision tree is lower than the preset loss threshold, that is, the first loss value corresponding to the generated first decision tree is lower than the preset loss threshold, and since the first loss value is positively correlated with the first number of first nodes in the first decision tree and the first weight of the first node, it can avoid the overfitting problem caused by excessive complexity (too many nodes) and the underfitting problem caused by oversimplification (too few nodes and too concentrated weights), thereby finding the best balance point, improving the classification accuracy of the decision tree, and accurately identifying the type of communication identification.

[0088] Figure 2 This is another flowchart of the decision tree construction method provided in the embodiment of the present application.

[0089] In some embodiments, as Figure 2 As shown, step S110 acquires first historical usage information generated when the target communication identifier uses different service types in the database, which may include step S111.

[0090] S111, obtaining, from a database, first historical call information generated when the target communication identifier uses a call service, first historical SMS information generated when the target communication identifier uses a SMS service, and first historical traffic information generated when the target communication identifier uses a traffic service.

[0091] Specifically, in the database, historical record information associated with the target communication identifier (for example, a specific telephone number or user account) can be obtained. The historical record information covers the call records of the target communication identifier when using the call service, which can be called the first historical call information, and also covers the SMS sending and receiving records when using the SMS service, which can be called the first historical SMS information, and the data traffic usage records when using the traffic service, which can be called the first historical traffic information.

[0092] Step S120 calculates the proportional relationship between the first historical usage information to obtain first historical cross information, which may include step S121.

[0093] S121 , determining first historical cross information according to a proportional relationship among the first historical call information, the first historical text message information, and the first historical traffic information.

[0094] Specifically, based on the relative proportion or relationship between the first historical call information, first historical SMS information and first historical traffic information generated by the target communication identifier when using call service, SMS service and traffic service, a comprehensive first historical cross-information can be determined, which reflects the intrinsic connection between different service types.

[0095] The embodiment of the present application obtains historical information of the target communication identifier when using call, text message and traffic services from the database, and calculates the proportional relationship between this information, so as to obtain a comprehensive first historical cross-information, which can reveal the intrinsic connection between different service types and provide a rich data foundation for the construction of the decision tree.

[0096] Figure 3 This is another flowchart of the decision tree construction method provided in the embodiment of the present application.

[0097] In some embodiments, as Figure 3 As shown, the first historical call information includes the number of first calls; the first historical SMS information includes the number of first SMS messages; the first historical traffic information includes the number of application usage; the first historical cross-information includes the first sub-historical cross-information, the second sub-historical cross-information and the third sub-historical cross-information; step S121 determines the first historical cross-information based on the proportional relationship between the first historical call information, the first historical SMS information and the first historical traffic information, and may include steps S1211 to S1213.

[0098] S1211: Divide the first number of call items by the first number of text messages to obtain first sub-history cross information.

[0099] The first number of calls is the total number of calls generated when the target communication identifier uses the call service within the first preset time period. The total number of calls can be called the first number of calls.

[0100] The first number of text messages is the total number of text messages generated when the target communication identifier uses the text message service within the first preset time period. The total number of text messages can be called the first number of text messages.

[0101] Specifically, by dividing the number of first calls generated when the target communication identifier uses the call service within the first preset time period by the number of first text messages generated when the target communication identifier uses the text message service, a specific ratio information can be obtained. The ratio information can be called the first sub-history cross-information. The first history cross-information reflects the usage preference or pattern of the target communication identifier between call and text message services.

[0102] For example, the first sub-history cross-information of a normal type of telephone number takes values ​​within a preset normal range; while the first sub-history cross-information of an abnormal type of telephone number may be too large or too small, such as the number of first calls is much larger than the number of first text messages, making the first sub-cross-information greater than the maximum value of the preset normal range, or the number of first text messages is much larger than the number of first communications, making the first sub-cross-information less than the minimum value of the preset normal range.

[0103] S1212: Divide the number of first call records by the number of application usage to obtain second sub-history cross information.

[0104] Specifically, by dividing the number of first calls generated by the target communication identifier using the call service during the first preset time period by the number of applications used when using the data service, a specific ratio information can be obtained. This ratio information can be referred to as the second sub-history cross-information. The second sub-history cross-information reflects the target communication identifier's usage preference or pattern between call and data services.

[0105] For example, the second sub-history cross-information of a normal type of phone number takes values ​​within a preset normal range; while the second sub-history cross-information of an abnormal type of phone number will be too large, such as the number of first calls is far greater than the number of applications used, making the second sub-cross-information greater than the maximum value of the preset normal range.

[0106] S1213: Divide the number of first short messages by the number of applications used to obtain third sub-history cross information.

[0107] Specifically, by dividing the number of first text messages generated by the target communication identifier when using the SMS service during the first preset time period by the number of application usages generated when using the data service, a specific ratio information can be obtained. This ratio information can be referred to as the third sub-history cross-information. The third sub-history cross-information reflects the target communication identifier's usage preference or pattern between SMS and data services.

[0108] For example, the third sub-historical cross-information of a normal type of phone number takes values ​​within the preset normal range; while the third sub-historical cross-information of an abnormal type of phone number will be too large, such as the number of first text messages is far greater than the number of applications used, making the third sub-cross-information greater than the maximum value of the preset normal range.

[0109] It should be noted that the purpose of cross-information is to present the differences in communication behaviors across different dimensions. Among the three dimensions of calls, text messages, and traffic, fraudulent numbers often only have primary behaviors in one dimension, so the ratio characteristics between the dimensions also make sense. Using the ratio of the first number of calls to the first number of text messages to cross-reference call characteristics with text message characteristics, if this characteristic is too large or too small, it is more likely to be a fraudulent number. In addition, using the ratio of the first number of calls to the number of applications used to cross-reference call characteristics with traffic characteristics, and using the first number of text messages to the number of applications used to cross-reference text message characteristics with traffic characteristics, the value of the fraudulent number will be much greater than that of a normal number.

[0110] The embodiment of the present application calculates the proportional relationship between the information generated by the target communication identifier between calls, text messages and traffic services (such as the number of calls, the number of text messages and the number of applications used), that is, the first, second and third sub-history cross-information, which can accurately reveal the usage preference or pattern of the target communication identifier, which is conducive to accurately identifying the type of the target communication identifier.

[0111] In some embodiments, the first historical call information also includes at least one of the following: number of active call days, call frequency, call duration, call device identification, number of call locations, number of call contact users, proportion of call times, proportion of calling users, proportion of call duration intervals, and regularity information of call time points.

[0112] The number of active call days is used to record how many days the user holding the target communication ID has made calls within the first preset period. The number of active call days can be simply understood as the number of days with call records within the first preset period.

[0113] It should be noted that normal users' call behavior is average and consistent throughout the first preset time period, so normal target communication identifiers have relatively more active days. However, call records from fraudulent numbers are typically more concentrated and may be intermittent, meaning they have relatively fewer active days. Therefore, the number of days a user has call records within the time span can be defined as active call days.

[0114] The call frequency is equal to the number of first calls divided by the number of active call days. It records the average number of calls made per day by the user holding the target communication identifier during the active call days, as well as the distribution of the number of calls. Normal numbers have a lower call frequency, while abnormal numbers have a higher call frequency.

[0115] It's important to note that since fraudulent numbers don't have a target contact, their primary characteristic is a significantly higher number of calls than legitimate numbers. However, call frequency is often affected by the number of active days. Therefore, we can focus our analysis on each active day to derive a daily call frequency. The average number of calls per day, or call frequency, can be calculated by dividing the total number of calls (number of first calls) by the total number of active days (number of active call days).

[0116] In one example, deeper insights can be extracted through distributional features. The first historical call information also includes call distribution characteristics. For each phone number, the call counts across active days, combined over a time span, form a distribution. Therefore, the median of active-day call counts can be used to represent call frequency, excluding the influence of extreme values; the maximum or minimum active-day call counts can be used to represent extreme values; the variance and standard deviation of active-day call counts can be used to represent the discrete trends and fluctuations in daily call counts; and the skewness of active-day call counts can be used to represent the degree of data distribution skewness. By incorporating distributional features of call counts over a time span, such as the median, maximum, minimum, variance, standard deviation, and skewness, deeper insights into user call behavior can be extracted, more comprehensively reflecting the user's call frequency, extreme values, discrete trends, fluctuations, and the degree of data distribution skewness.

[0117] Call duration is equal to the total call duration divided by the number of first calls. It is used to record the average duration of each call and the distribution of call duration. Calls to normal numbers have longer durations, while calls to abnormal numbers have shorter durations.

[0118] In one example, for the distribution of call duration, we can calculate the average call duration per phone number, the central tendency (median) of call duration excluding extreme cases, the extreme cases (maximum and minimum), and the fluctuation and dispersion of call duration (variance and standard deviation). By calculating distribution characteristics such as the mean, median, maximum, minimum, variance, and standard deviation of call duration, we can gain a more comprehensive understanding of the central tendency, extreme cases, and fluctuation dispersion of call duration, providing strong support for in-depth analysis of calling behavior.

[0119] It should be noted that the embodiment of the present application eliminates the influence of time span by averaging the total features (such as the number of first calls, the number of active call days, and the total call duration) to days or months, and obtains mean features (such as call frequency and call duration), which can make the call features more distinguishable for numbers of different categories.

[0120] The device identifier and number of call locations are used to record how many different devices a user uses and where they make calls. Fraudulent numbers may frequently change devices or locations.

[0121] The number of call contacts is used to record how many different people a user has called. Legitimate numbers often have a fixed social circle and a limited number of call contacts. Scam numbers, however, have no target contacts and typically call each contact individually, with few duplicates. Therefore, the number of call contacts can be very high.

[0122] In one example, the social relationships of each number were taken into account, and the fluctuations in the average number of calls per month and the average duration of each call were used to indicate the closeness of the relationship with the contact. Due to the lack of social relationships, fraudulent numbers would perform differently from normal numbers on these two variables.

[0123] It should be noted that since fraudulent calls are often proactive, the number and duration of calls can be considered when constructing caller characteristics.

[0124] The proportion of outbound calls is equal to the total number of outbound calls divided by the number of first calls. Fraudulent calls are often initiated, so the proportion of outbound calls is higher.

[0125] The proportion of calling users is equal to the number of users who initiated calls divided by the number of users contacted by the call. Fraudulent calls are often initiated, so the proportion of calling users is higher.

[0126] The call duration interval ratio records the segmented ratio characteristics of the call duration of the caller. For example, the call duration can be divided into six intervals: [0-10), [10-30), [30-60), [60-300), [300-1800), and [1800-+∞), representing six different levels of calls (very short calls, relatively short calls, short calls, long calls, relatively long calls, and very long calls). By classifying all call records for each target communication identifier into these six intervals according to the call duration, the frequency of each interval can be obtained. Dividing it by the total number of calls can obtain the call duration interval ratio of each interval. For example, in the first preset time period, 50 call records were generated, 45 of which were in the interval [0-10) min and 5 were in the interval [10-30) min. Therefore, by dividing the call duration of each call record into the corresponding interval, the call duration interval ratio of each interval can be obtained as 0.9, 0.1, 0, 0, and 0.

[0127] Call time pattern information is used to record users' call status in different time periods, such as the difference in calls on weekdays and weekends.

[0128] It's important to note that fraudulent calls are most common during weekdays, but significantly less common on weekends. This is a clear distinction, whereas legitimate numbers generally don't have this same contrast. Therefore, we divide call records into weekday and weekend call records based on the date of the call.

[0129] In one example, call types are differentiated to obtain four call record tables: weekday callers, weekday callees, weekend callers, and weekend callees, which can make feature construction more efficient.

[0130] The first historical SMS information also includes at least one of the following: SMS active days, SMS frequency, number of users contacted by SMS, average number of users contacted by SMS per day, SMS type, and SMS time point regularity information.

[0131] The number of active SMS days refers to the number of days with SMS records within the first preset period.

[0132] SMS frequency is equal to the number of first SMS messages divided by the number of days SMS messages are active. It should be noted that in SMS frequency analysis, given that fraudulent SMS messages are likely sent in bulk, the time interval between two messages will often contain many zeros, making the method of expressing frequency using time interval ineffective. Therefore, in SMS frequency analysis, the time interval feature is removed, and the SMS frequency is more accurately calculated by dividing the number of first SMS messages by the number of days SMS messages are active.

[0133] The number of SMS contacts is used to record how many different people a user has communicated with through SMS.

[0134] The average number of users contacted by SMS per day is equal to the number of users contacted by SMS divided by the number of days SMS is active.

[0135] The SMS type includes the sending type and the receiving type.

[0136] SMS time point regularity information is used to record users' SMS communication in different time periods, such as the difference in SMS messages on weekdays and weekends.

[0137] In one example, the first historical SMS information also includes SMS distribution characteristics. By analyzing the average monthly SMS count for each contact number, distribution characteristics such as mean, median, maximum, minimum, variance, standard deviation, and skewness are constructed. This provides a more comprehensive understanding of SMS concentration trends, extreme cases, and the degree of fluctuation dispersion, providing strong support for in-depth analysis of SMS behavior.

[0138] The first historical traffic information also includes at least one of the number of active traffic days and the total traffic.

[0139] Among them, the number of days with active traffic is the number of days with traffic usage records within the first preset time period.

[0140] The total flow is the total amount of flow used in the first preset time period.

[0141] In one example, the first historical traffic information also includes traffic distribution characteristics. For each number, the average monthly traffic usage across all applications forms a distribution. Based on this distribution, distribution characteristics such as mean, median, maximum, minimum, variance, standard deviation, and skewness can be calculated. Since fraudulent numbers typically do not generate much entertainment traffic, their distribution characteristics can distinguish them from legitimate numbers.

[0142] The embodiment of the present application collects first historical call information (including the number of active call days, call frequency, call duration, call device identification, number of call locations, number of call contact users, proportion of call times, proportion of calling users, proportion of call duration intervals, regular information on call time points, etc.) and first historical SMS information and traffic information (covering the number of active SMS days, SMS frequency, number of SMS contact users, average number of SMS contact users per day, SMS type, regular information on SMS time points, number of active traffic days, total traffic, etc.), which can effectively identify the differences in behavioral patterns between normal users and abnormal (such as fraudulent) numbers, and can accurately reveal the usage preferences or patterns of the target communication identification, providing a rich data foundation for the construction of a decision tree.

[0143] In one example, when constructing call and SMS features, the contact user's related characteristics were incorporated. The average monthly number of calls or call duration for each contact user in the premium number records was used to reflect the closeness of each contact user's social relationship with the user holding the target communication identifier. This allows the target communication identifier's social relationship type to be determined.

[0144] Figure 4 This is another flowchart of the decision tree construction method provided in the embodiment of the present application.

[0145] In some embodiments, as Figure 4 As shown, step S130 generates information of an initial decision tree based on the first historical usage information and the first historical cross information, and may include steps S131 and S132.

[0146] S131 , adding the amount of the first historical usage information and the amount of the first historical intersection information to obtain the initial number of initial nodes in the initial decision tree, wherein the initial nodes correspond one-to-one to the first historical usage information and the first historical intersection information.

[0147] Specifically, since the initial node corresponds one-to-one to the first historical usage information and the first historical cross-information, that is, each type of first historical usage information corresponds to an initial node, and each type of first historical cross-information corresponds to an initial node, the number of first historical usage information and first historical cross-information is added together to obtain the total number of initial nodes in the initial decision tree, that is, the initial number.

[0148] For example, the first historical call information includes the number of first calls within the first preset time period, the number of active call days, call frequency, call duration, call device identification, number of call locations, number of call contact users, proportion of call times, proportion of calling users, proportion of call duration intervals, and regular information on call time points, then 11 initial nodes can be created.

[0149] S132, from the correspondence between preset historical usage information, historical cross information and preset weights, obtain target preset weights corresponding to the first historical usage information and the first historical cross information respectively, and use the target preset weights as the initial weights of the corresponding initial nodes.

[0150] The preset weight can be set based on the degree of influence on the target communication identification being an abnormal type. For example, the corresponding preset weight can be set based on the number of first calls, the number of active call days, the call frequency, the call duration, the call device identifier, the number of call locations, the number of call contact users, the proportion of call times, the proportion of call users, the proportion of call duration intervals, and the regularity of call time points within the first preset time period. For another example, the corresponding preset weight can be set based on the first sub-history cross-information, the second sub-history cross-information, and the third sub-history cross-information within the first preset time period.

[0151] Specifically, in the correspondence between the preset historical usage information, historical cross information and preset weights, the target preset weights that match the first historical usage information and the first historical cross information can be found, and these target preset weights can be set as the initial weights of the corresponding initial nodes in the decision tree.

[0152] The embodiment of the present application can generate an initial decision tree by combining the first historical usage information and the first historical cross-information, wherein the initial nodes of the initial decision tree correspond one-to-one to the first historical usage information and the first historical cross-information, and initial weights can be assigned to these initial nodes according to the target preset weights corresponding to the first historical usage information and the first historical cross-information (the preset weights are set based on the degree of their influence on the target communication identifier being judged as an abnormal type), thereby achieving efficient and accurate decision tree initialization based on historical data, and providing a solid foundation for subsequent communication identifier type classification tasks.

[0153] In some embodiments, the first loss value is positively correlated with the first quantity and the first weight, respectively, and includes a loss function (1):

[0154]

[0155] Among them, LOSS is the loss value of the decision tree (such as the first loss value), a and b are preset constants, N is the number of nodes (such as the first number of first nodes), and Wi is the node weight (such as the first weight of the first node).

[0156] It should be noted that the more features and nodes there are, the longer it takes to train the decision tree. Furthermore, as the number of features increases, the constructed decision tree becomes more complex, leading to a decrease in its generalization ability. Therefore, the loss function (1) can be used to find the most appropriate feature subset among all the features in the dataset to improve the training efficiency of the decision tree.

[0157] Specifically, the loss value LOSS represents the difference between the predicted result and the true result.

[0158] a is a preset constant used to adjust the contribution of structural complexity to the total loss.

[0159] b is a preset constant used to adjust the contribution of node weight to the total loss. b is a positive number.

[0160] N is the number of nodes in the decision tree, which directly reflects the complexity of the decision tree. The more nodes there are, the more complex the tree structure is, and the higher the risk of overfitting may be. By incorporating the number of nodes into the loss function (1), the decision tree can be constructed to maintain simplicity and avoid unnecessary branches.

[0161] The loss function (1) aims to find a balance point so that the decision tree is neither too complex (leading to overfitting) nor too simple (leading to underfitting).

[0162] Wi is the node weight of the decision tree. If the weights of some nodes in the decision tree are higher (indicating that these nodes have a greater influence or contribution to the prediction results), then ||w|| 2 The value of will increase, which may lead to an increase in the LOSS value. Therefore, when building a decision tree, it is possible to avoid generating too many high-weight nodes to prevent overfitting.

[0163] In the embodiment of the present application, by adding a penalty term for the complexity of the decision tree when calculating the first loss value, the risk of overfitting can be considered when constructing the decision tree, thereby tending to select a simpler but sufficiently accurate decision tree. At the same time, when constructing the decision tree, the generation of too many high-weight nodes can be further avoided, thereby preventing underfitting. By incorporating the complexity of the decision tree and the node weights into the loss function, the model complexity and accuracy can be effectively balanced when constructing the decision tree, avoiding overfitting and underfitting, and constructing a simpler and sufficiently accurate decision tree.

[0164] It should be noted that after the first decision tree is constructed, historical data within the second preset time period can be used to continue to be optimized to further enhance the classification accuracy of the decision tree.

[0165] Figure 5 This is a flow chart of optimizing the first decision tree in the decision tree construction method provided in an embodiment of the present application.

[0166] In some embodiments, as Figure 5 As shown, the decision tree construction method may further include steps S151 to S156.

[0167] S151 , obtaining, from a database, second historical usage information generated when a target communication identifier uses different service types within a second preset period of time and a historical real type to which the target communication identifier belongs.

[0168] Specifically, the historical usage information of the target communication identifier using different service types (such as voice calls, text messages, traffic services, etc.) within the second preset time period can be obtained in the database. This historical usage information can be called second historical usage information. The historical real type of the target communication identifier within the second preset time period can also be obtained, such as an abnormal type.

[0169] S152: Calculate the proportional relationship between the second historical usage information to obtain second historical cross information.

[0170] Specifically, by analyzing the relative sizes or ratios of the second historical usage information of different service types, new data that can reflect the association relationship between these historical usage information can be obtained. The new data can be called second historical cross information.

[0171] S153: Input the second historical usage information and the second historical intersection information into corresponding first nodes in the first decision tree respectively to obtain a plurality of historical sub-results.

[0172] Specifically, the second historical usage information and the second historical cross information may be respectively input into respective nodes of the corresponding first decision tree to obtain prediction results output by respective nodes, which may be referred to as historical sub-results.

[0173] In one example, the second historical usage information and the second historical cross information are respectively subjected to One-hot encoding processing, and the new variables after processing have only two values ​​0 and 1. In this way, after the qualitative feature variables are subjected to One-hot encoding processing, the calculation of the distance between the feature variables will be more reasonable.

[0174] S154: Multiply the historical sub-results by the corresponding first weights respectively and add them together to obtain the historical results.

[0175] Specifically, the historical sub-results output by each node of the first decision tree are multiplied by the corresponding node weight and then added together to obtain the prediction result of the first decision tree, which can be called the historical result.

[0176] S155, comparing the historical result with the historical true type to obtain an error value.

[0177] Specifically, the prediction error value of the first decision tree can be obtained by comparing the historical results predicted by the first decision tree for the target communication identifier within the second preset time period with the historical actual type of the target communication identifier within the second preset time period. For example, if the historical actual type is an abnormal type, and the probability that the historical result is an abnormal type is 0.9, the prediction error value is 0.1. For another example, if the historical actual type is an abnormal type, and the probability that the historical result is an abnormal type is 0.7, the prediction error value is 0.3.

[0178] S156. When the error value is greater than the preset error threshold, adjust the first quantity and the first weight to obtain information of the second decision tree, wherein the information of the second decision tree includes the second node and its corresponding second quantity and second weight, and the second loss value corresponding to the second decision tree is less than the preset loss threshold, and the second loss value is positively correlated with the second quantity and the second weight, respectively.

[0179] Specifically, when the prediction error value of the first decision tree is greater than the preset error threshold (such as greater than 0.4), the number of nodes and the node weights of the first decision tree can be adjusted, and the loss function (1) still needs to be satisfied during the adjustment, so that the second loss value of the adjusted second decision tree is less than the preset loss threshold, and the corresponding prediction error value when the second decision tree predicts the type of the target communication identifier within the second preset time period is less than the preset error threshold.

[0180] The embodiment of the present application can significantly improve the accuracy of the decision tree in classifying the target communication identification type and reduce the prediction error value by continuously optimizing the decision tree, using historical data within the second preset time period to calculate cross-information and adjust the nodes and weights of the first decision tree.

[0181] Figure 6 This is a flow chart of using a second decision tree to determine a communication identification type in the decision tree construction method provided in an embodiment of the present application.

[0182] In some embodiments, as Figure 6 As shown, the decision tree construction method may further include steps S161 to S164.

[0183] S161 , obtaining, from a database, third historical usage information generated when a target communication identifier uses different service types within a third preset time period.

[0184] Specifically, historical usage information of the target communication identifier using different service types (such as voice calls, text messages, traffic services, etc.) within a third preset time period can be obtained in the database. This historical usage information can be called third historical usage information.

[0185] S162: Calculate the proportional relationship between the third historical usage information to obtain third historical intersection information.

[0186] Specifically, by analyzing the relative sizes or ratios of the third historical usage information of different service types, new data that can reflect the association relationship between the third historical usage information can be obtained. The new data can be called third historical cross information.

[0187] S163: Input the third historical usage information and the third historical intersection information into corresponding second nodes in the second decision tree respectively to obtain multiple sub-results.

[0188] Specifically, the third historical usage information and the third historical cross information may be respectively input into respective nodes of the corresponding second decision tree to obtain prediction results output by respective nodes, which may be referred to as sub-results.

[0189] S164: Multiply the multiple sub-results by the corresponding second weights and add them together to obtain the target result.

[0190] The target result is the communication identification type to which the target communication identification determined by the second decision tree belongs within the third preset time period.

[0191] Specifically, the sub-results output by each node of the second decision tree are multiplied by the corresponding node weights and then added together to obtain the prediction result of the second decision tree, which can be called the target result.

[0192] In this embodiment, the third historical usage information of the target communication identifier within a third preset time period and the calculated third historical cross-information are input into each node of the second decision tree. The target result is then obtained by combining the predicted sub-results of each node with the node weight. By comprehensively considering the usage of different service types and their correlations, and using the tuned second decision tree, the type of the target communication identifier can be more accurately predicted, thereby improving the reliability of the prediction result.

[0193] In an example, the top five important features obtained using the LOSS comparison results as the judgment criteria are voc_num_per_day (indicating the average number of call contacts per day), voc_call_num_per_day (indicating the average number of calling contacts per day), sms_cover_day_month (indicating the average number of SMS active days per month), sms_cover_day_count_sem (indicating the fluctuation in the number of SMS messages on each active day), and voc_imei_num (indicating the number of different call devices used), which are in line with common sense logic. voc_call_num_per_day (indicating the average number of calling contacts per day), the larger the value of this feature, the greater its positive effect on the predicted probability, that is, the more calling contacts there are on average per day, the more likely it is to be a fraudulent number; sms_cover_day_month (indicating the average number of SMS active days per month), the larger the value of this feature, the easier it is to lower the predicted probability of the positive sample, indicating that the longer the average monthly active days of SMS, the greater the possibility of it being a normal number; voc_imei_num (indicating the number of different calling devices used), the larger the value of this feature, the greater its positive effect on the predicted probability, that is, the more calling devices a number uses, the more likely it is to be a fraudulent number.

[0194] In another example, when tuning the first decision tree, the corresponding predicted probability is output on the test set, recorded as P1, and then it is sorted and divided into 10 probability intervals of equal length, such as [0, 0.1], [0.1, 0.2], ..., [0.9, 1]. Then, based on the first decision tree, the new sample is predicted, and the predicted probability is recorded as p1, and p1 is also divided in the same way. The actual sample proportion is the sample proportion of p1 in each interval. The expected sample proportion is the sample proportion of P1 in each interval. If the first decision tree is relatively stable, the sample proportions of different batches of data in each interval will not differ greatly, so the optimal decision tree can be determined.

[0195] In another example, by introducing the user fraud risk level indicator: in the prediction results of the second decision tree, the fraud probability of the fraud user is further divided into 5 groups with values ​​such as 0~0.2, 0.2~0.4, 0.4~0.6, 0.6~0.8, and 0.8~1, and assigned levels of 1 to 5 respectively. The greater the fraud risk level, the greater the probability that the user holding the target communication identifier is a fraud user, and the fraud user can be judged more accurately.

[0196] Based on the same inventive concept, in a second aspect, the embodiment of the present application further provides a device for constructing a decision tree, wherein the decision tree is used to determine the type of communication identification, such as Figure 7As shown, the apparatus 700 may include an acquisition module 710 , a calculation module 720 , a generation module 730 and an adjustment module 740 .

[0197] An acquisition module 710 is configured to acquire, from a database, first historical usage information generated when a target communication identifier uses different service types within a first preset time period;

[0198] A calculation module 720 is configured to calculate a proportional relationship between the first historical usage information to obtain first historical cross information;

[0199] A generating module 730 is configured to generate information of an initial decision tree based on the first historical usage information and the first historical cross information, wherein the information of the initial decision tree includes an initial number of initial nodes and an initial weight of each initial node;

[0200] Adjustment module 740 is used to adjust the initial quantity and initial weight to obtain information of the first decision tree, wherein the information of the first decision tree includes the first node and its corresponding first quantity and first weight, and the first loss value corresponding to the first decision tree is less than the preset loss threshold, and the first loss value is positively correlated with the first quantity and the first weight, respectively.

[0201] According to the decision tree construction device provided in the embodiment of the present application, the device obtains detailed first historical usage information generated by the target communication identifier when using various services within a first preset time period from a database, and then can accurately calculate the proportional relationship between this information to obtain first historical cross-information. The first historical cross-information reflects the inherent connection between different communication behaviors, helps to more accurately describe the user's communication behavior characteristics, and provides a rich data foundation for the construction of the decision tree. Then, based on the first historical usage information and the first historical cross-information, information of the initial decision tree can be generated, such as information about the initial nodes in the initial decision tree, the initial number of initial nodes, and the initial weights of the initial nodes. Then, by carefully adjusting the initial number and initial weights of the initial nodes, the decision tree structure is continuously optimized until the loss value corresponding to the decision tree is lower than the preset loss threshold, that is, the first loss value corresponding to the generated first decision tree is lower than the preset loss threshold, and since the first loss value is positively correlated with the first number of first nodes in the first decision tree and the first weight of the first node, it can avoid the overfitting problem caused by excessive complexity (too many nodes) and the underfitting problem caused by oversimplification (too few nodes and too concentrated weights), thereby finding the best balance point, improving the classification accuracy of the decision tree, and accurately identifying the type of communication identification.

[0202] In some embodiments, the acquisition module is used to acquire, from a database, first historical usage information generated when the target communication identifier uses different service types, which can be specifically used to:

[0203] Obtaining, from a database, first historical call information generated when the target communication identifier uses a call service, first historical text message information generated when the target communication identifier uses a text message service, and first historical traffic information generated when the target communication identifier uses a traffic service;

[0204] Calculate the proportional relationship between the first historical usage information to obtain the first historical cross information, including:

[0205] The first historical cross information is determined according to a proportional relationship among the first historical call information, the first historical text message information, and the first historical traffic information.

[0206] In some embodiments, the first historical call information includes the number of first calls; the first historical text message information includes the number of first text messages; the first historical traffic information includes the number of application usage; the first historical cross-information includes first sub-historical cross-information, second sub-historical cross-information, and third sub-historical cross-information; the acquisition module is used to determine the first historical cross-information based on the proportional relationship between the first historical call information, the first historical text message information, and the first historical traffic information, and can specifically be used to:

[0207] Divide the first number of call items by the first number of text messages to obtain first sub-history cross information;

[0208] Divide the first call count by the number of application usages to obtain second sub-history cross information;

[0209] The third sub-history cross information is obtained by dividing the first number of short messages by the number of application usages.

[0210] In some embodiments, the first historical call information further includes at least one of the following: number of active call days, call frequency, call duration, call device identifier, number of call locations, number of call contact users, proportion of call times, proportion of calling users, proportion of call duration intervals, and regularity of call time points;

[0211] The first historical SMS information also includes at least one of the following: number of active SMS days, SMS frequency, number of users contacted by SMS, average number of users contacted by SMS per day, SMS type, and regularity of SMS timing;

[0212] The first historical traffic information also includes at least one of the number of active traffic days and the total traffic.

[0213] In some embodiments, the generation module is used to generate information of an initial decision tree based on the first historical usage information and the first historical cross information, which can be specifically used to:

[0214] Adding the amount of the first historical usage information and the amount of the first historical cross information to obtain an initial number of initial nodes in the initial decision tree, wherein the initial nodes correspond one-to-one to the first historical usage information and the first historical cross information;

[0215] From the correspondence between preset historical usage information, historical cross information and preset weights, the target preset weights corresponding to the first historical usage information and the first historical cross information are obtained, and the target preset weights are used as the initial weights of the corresponding initial nodes.

[0216] In some embodiments, the first loss value is positively correlated with the first quantity and the first weight, respectively, including:

[0217]

[0218] Where LOSS is the first loss value, a and b are preset constants, N is the first number of the first node, W i is the first weight of the first node.

[0219] In some embodiments, the apparatus further comprises a comparison module:

[0220] The acquisition module is further configured to acquire, from a database, second historical usage information generated when the target communication identifier uses different service types within a second preset time period and a historical real type to which the target communication identifier belongs;

[0221] The calculation module is further used to calculate the proportional relationship between the second historical usage information to obtain the second historical cross information;

[0222] The calculation module is further configured to input the second historical usage information and the second historical cross information into corresponding first nodes in the first decision tree, respectively, to obtain a plurality of historical sub-results;

[0223] The calculation module is further configured to multiply the historical sub-results by the corresponding first weights and then add the results to obtain the historical results;

[0224] The comparison module is used to compare the historical results with the historical real types to obtain the error value;

[0225] When the error value is greater than the preset error threshold, the first quantity and the first weight are adjusted to obtain information of the second decision tree, wherein the information of the second decision tree includes the second node and its corresponding second quantity and second weight, and the second loss value corresponding to the second decision tree is less than the preset loss threshold, and the second loss value is positively correlated with the second quantity and the second weight, respectively.

[0226] In some embodiments, further comprising:

[0227] The acquisition module is further configured to acquire, from a database, third historical usage information generated when the target communication identifier uses different service types within a third preset time period;

[0228] The calculation module is further used to calculate the proportional relationship between the third historical usage information to obtain the third historical cross information;

[0229] The calculation module is further configured to input the third historical usage information and the third historical cross information into corresponding second nodes in the second decision tree, respectively, to obtain a plurality of sub-results;

[0230] The calculation module is further used to multiply the multiple sub-results by the corresponding second weights and then add them together to obtain the target result.

[0231] Each module in the decision tree construction device provided in the embodiment of the present application can realize Figures 1 to 6 The functions of each step of the provided decision tree construction method and its ability to achieve corresponding technical effects are described briefly and will not be repeated here.

[0232] Figure 8 A schematic diagram of the hardware structure of a decision tree construction device provided in an embodiment of the present application is shown.

[0233] The decision tree construction device may include a processor 801 and a memory 802 storing computer program instructions.

[0234] Specifically, the processor 801 may include a central processing unit (CPU) or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0235] Memory 802 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 802 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 802 may include removable or non-removable (or fixed) media. Where appropriate, memory 802 may be internal or external to the decision tree construction device. In a specific embodiment, memory 802 is a non-volatile solid-state memory.

[0236] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present disclosure.

[0237] The processor 801 reads and executes computer program instructions stored in the memory 802 to implement any one of the decision tree construction methods in the above embodiments.

[0238] In one example, the decision tree construction device may further include a communication interface 803 and a bus 804. Figure 8 As shown, the processor 801, the memory 802, and the communication interface 803 are connected via a bus 804 and communicate with each other.

[0239] The communication interface 803 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0240] The bus 804 includes hardware, software, or both, coupling the components of the decision tree construction device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Linear Predictive Coding (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (Peripheral Component Interconnect-X, PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VESA Local Bus, VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 804 may include one or more buses. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0241] The device can execute the decision tree construction method in the embodiment of the present application based on each unit / component in the decision tree construction device, thereby realizing the combination of Figures 1 to 6 Describe the decision tree construction method.

[0242] In addition, in conjunction with the decision tree construction method in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the decision tree construction methods in the above embodiments is implemented.

[0243] The present application also provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes each process of implementing any one of the above-mentioned decision tree construction method embodiments.

[0244] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0245] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The programs or code segments can be stored in a machine-readable medium, or transmitted on a transmission medium or communication link via a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memories (ROMs), flash memories, erasable read-only memories (EROMs), floppy disks, compact disc read-only memories (CD-ROMs), optical discs, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet and intranets.

[0246] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0247] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0248] The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application.

Claims

1. A method for constructing a decision tree, characterized in that: The decision tree is used to determine the communication identification type, and the method includes: Acquiring, from a database, first historical usage information generated when a target communication identifier uses different service types within a first preset time period; Calculating a proportional relationship between the first historical usage information to obtain first historical cross information; Generate information of an initial decision tree based on the first historical usage information and the first historical intersection information, wherein the information of the initial decision tree includes initial nodes and their corresponding initial numbers and initial weights; Adjusting the initial quantity and the initial weight to obtain information of a first decision tree, wherein the information of the first decision tree includes a first node and its corresponding first quantity and first weight, and a first loss value corresponding to the first decision tree is less than a preset loss threshold, and the first loss value is positively correlated with the first quantity and the first weight, respectively; The acquiring, in the database, first historical usage information generated when the target communication identifier uses different service types includes: Obtaining, from a database, first historical call information generated when the target communication identifier uses a call service, first historical text message information generated when the target communication identifier uses a text message service, and first historical traffic information generated when the target communication identifier uses a traffic service; The calculating the proportional relationship between the first historical usage information to obtain the first historical cross information includes: First historical cross information is determined according to a proportional relationship among the first historical call information, the first historical text message information, and the first historical traffic information.

2. The method according to claim 1, characterized in that The first historical call information includes the number of first calls; the first historical text message information includes the number of first text messages; the first historical traffic information includes the amount of application usage; the first historical cross-information includes first sub-historical cross-information, second sub-historical cross-information, and third sub-historical cross-information; and determining the first historical cross-information based on a proportional relationship among the first historical call information, the first historical text message information, and the first historical traffic information includes: Divide the first number of call messages by the first number of text messages to obtain the first sub-history cross information; Divide the first call count by the number of applications used to obtain the second sub-history cross information; The third sub-history cross information is obtained by dividing the first number of short messages by the number of times the application is used.

3. The method according to claim 2, characterized in that The first historical call information also includes at least one of the following: number of active call days, call frequency, call duration, call device identifier, number of call locations, number of call contact users, proportion of call times, proportion of calling users, proportion of call duration intervals, and regularity of call time points; The first historical SMS information further includes at least one of the following: number of active SMS days, SMS frequency, number of users contacted by SMS, average number of users contacted by SMS per day, SMS type, and regularity of SMS timing; The first historical traffic information also includes at least one of traffic active days and total traffic.

4. The method according to claim 1, wherein The information for generating an initial decision tree based on the first historical usage information and the first historical cross information includes: Adding the first historical usage information and the first historical intersection information to obtain an initial number of initial nodes in the initial decision tree, wherein the initial nodes correspond one-to-one to the first historical usage information and the first historical intersection information; From the correspondence between preset historical usage information, historical cross information and preset weights, the target preset weights corresponding to the first historical usage information and the first historical cross information are obtained, and the target preset weights are used as the initial weights of the corresponding initial nodes.

5. The method according to claim 1, wherein The first loss value is positively correlated with the first quantity and the first weight, respectively, including: Wherein, LOSS is the first loss value, a and b are preset constants, N is the first number of the first nodes, W i is the first weight of the first node.

6. The method according to claim 1, characterized in that Also includes: Acquire, from a database, second historical usage information generated when the target communication identifier uses different service types within a second preset time period and a historical real type to which the target communication identifier belongs; calculating a proportional relationship between the second historical usage information to obtain second historical cross information; inputting the second historical usage information and the second historical intersection information into the corresponding first nodes in the first decision tree respectively to obtain a plurality of historical sub-results; Multiplying each of the historical sub-results by the corresponding first weight and then adding the results to obtain a historical result; Comparing the historical result with the historical true type to obtain an error value; When the error value is greater than a preset error threshold, the first quantity and the first weight are adjusted to obtain information of a second decision tree, wherein the information of the second decision tree includes a second node and its corresponding second quantity and second weight, and the second loss value corresponding to the second decision tree is less than the preset loss threshold, and the second loss value is positively correlated with the second quantity and the second weight, respectively.

7. The method according to claim 6, characterized in that Also includes: Acquiring, from a database, third historical usage information generated when the target communication identifier uses different service types within a third preset time period; Calculating a proportional relationship between the third historical usage information to obtain third historical cross information; inputting the third historical usage information and the third historical intersection information into corresponding second nodes in the second decision tree respectively to obtain a plurality of sub-results; The target result is obtained by multiplying the multiple sub-results by the corresponding second weights and then adding them together.

8. A decision tree construction device, characterized in that: The decision tree is used to determine the communication identification type, and the device includes: An acquisition module, configured to acquire, from a database, first historical usage information generated when a target communication identifier uses different service types within a first preset time period; a calculation module, configured to calculate a proportional relationship between the first historical usage information to obtain first historical cross information; a generating module, configured to generate information of an initial decision tree based on the first historical usage information and the first historical cross information, wherein the information of the initial decision tree includes an initial number of initial nodes and an initial weight of each initial node; an adjustment module, configured to adjust the initial quantity and the initial weight to obtain information of a first decision tree, wherein the information of the first decision tree includes a first node and its corresponding first quantity and first weight, and a first loss value corresponding to the first decision tree is less than the preset loss threshold, and the first loss value is positively correlated with the first quantity and the first weight, respectively; The acquisition module is used to acquire, from a database, first historical usage information generated when the target communication identifier uses different service types, including: An acquisition module, configured to acquire, from a database, first historical call information generated when the target communication identifier uses a call service, first historical text message information generated when the target communication identifier uses a text message service, and first historical traffic information generated when the target communication identifier uses a traffic service; The calculation module is configured to calculate a proportional relationship between the first historical usage information to obtain first historical cross information, including: The calculation module is used to determine the first historical cross information according to the proportional relationship between the first historical call information, the first historical text message information and the first historical traffic information.

9. A decision tree construction device, characterized in that: The device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the decision tree construction method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for constructing a decision tree according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of a device, the device is enabled to perform the decision tree construction method according to any one of claims 1 to 7.

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