55 results about "Maximal information coefficient" patented technology

The invention discloses a method for detecting network traffic anomaly based on feature selection and density peak clustering. The method comprises the following stages: a stage of acquiring the traffic: monitoring a network through a network analysis tool, and acquiring monitored data packets in the local; a stage of extracting features: extracting the data packets belonging to the same stream from the data packets, performing feature extraction of the data packets, and normalizing the extracted features; a stage of selecting the features: evaluating the importance of each feature on classification decision by utilizing a maximal information coefficient, simply clustering the features according to the redundancy among the features, selecting one feature having the highest importance, and adding the feature having the highest importance into a feature sub-set; and a stage of clustering and analyzing: clustering the features by adopting an improved clustering method based on a density peak so as to obtain clusters in a plurality of traffic types, performing little sampling of the cluster in each traffic type, performing class detection, and covering the traffic types of the clusters in the whole traffic types by utilizing the modal classified traffic types in a sampled sample, such that the anomaly traffic can be detected.

The invention relates to the field of intelligent water affairs, and discloses a residential water consumption prediction method based on an MIC-XGBoost algorithm, and the method comprises the steps: obtaining influence factor values and water consumption of each month, constructing a water consumption-influence factor value corresponding table of water consumption influence factors, and employing a maximum information coefficient algorithm to obtain a water consumption-influence factor value corresponding table of the water consumption influence factors; calculating a target influence factor which has the maximum influence on the water consumption, training the original XGBoost algorithm model according to the water consumption and the influence factor numerical value of each month to obtain a target XGBoost algorithm model, and predicting the water consumption by utilizing the target XGBoost algorithm model according to the influence factor numerical value of the current month to obtain the predicted water consumption of the current month. The invention further provides a resident water consumption prediction device based on the MIC-XGBoost algorithm, electronic equipment and a computer readable storage medium. According to the method, the problem that the prediction accuracy is low when the water consumption of residents is predicted by only depending on the XGBoost algorithm can be solved.

A method to identify a relationship between at least pairs of variables in a dataset generates a novel measure of dependence, referred to as a Maximal Information Coefficient (MIC), that captures a wide range of interesting associations (not limited to specific function types such as linear, exponential or periodic, or to all functional relationships), and that gives similar scores to equally noisy relationships of different types. MIC is based on the idea that if a relationship exists between two variables, then a grid can be drawn on a scatterplot of the two variables that partitions the data to encapsulate that relationship.

The embodiment of the invention provides a hydroelectric generating set vibration trend prediction method and system. The method comprises the steps: collecting an original signal feature set of a hydroelectric generating set; analyzing the correlation between each intrinsic mode component and the environment variable based on a maximum information coefficient (MIC), and extracting the environment variable of which the correlation is greater than a preset correlation threshold as the environment feature of each intrinsic mode component; forming a feature input sequence by each intrinsic mode component and the corresponding environment feature, taking the feature input sequence as the input of a neural network according to a time sequence, and performing neural network training to obtain a vibration trend prediction model; and enabling each intrinsic mode component of the current hydroelectric generating set to be predicted and the corresponding environmental characteristics to form a characteristic input vector, inputting the characteristic input vector into the vibration trend prediction model for prediction, and fusing all prediction results to obtain a future trend prediction value of the hydroelectric generating set, so that the precision and stability of the vibration trend prediction result are greatly improved.

The invention provides a control tower control effectiveness evaluation index association relationship analysis method, which comprises the following steps of: firstly, cleaning original data, removing abnormal values and repeated values, and filling missing values; selecting a part of calculable representative indexes of the air traffic control tower based on a process method, establishing a maximum interaction information method non-parametric model between the index pairs, and solving a maximum information coefficient (MIC) to determine the correlation strength between the indexes; and identifying the associated direction in an assisted manner according to a Spearman correlation coefficient method, and finally, obtaining the air traffic control tower index association relationship. By using the Spearman correlation coefficient method, the problem that the maximum nonparametric model cannot determine the correlation direction between the indexes is solved, the limitation and accuracyof the Spearman correlation coefficient method for judging the correlation degree between the nonlinear indexes are improved through the maximum nonparametric model, and a foundation is laid for improving the control efficiency of a control tower.

The invention relates to the technical field of industry user load prediction, in particular to an industry user electric quantity prediction method based on mode extraction and error adjustment, and the method comprises the following operation steps: S1, employing a K-means clustering algorithm to extract a typical industry annual load curve; s2, analyzing the correlation degree between external macroscopic factors such as holidays and festivals, weather and the like and the electric quantity of the user to be predicted by using a maximum information coefficient method; s3, according to a typical industry load curve and external factors such as holidays and festivals, weather and the like, electric quantity prediction is carried out by utilizing a sparrow to search the optimized BP neural network; s4, a non-parameter estimation method is utilized, probability distribution of residual errors is obtained, a prediction result is adjusted, and a final electric quantity predicted value is obtained.According to the method, the maximum information coefficient method is adopted, the influence degree of external factors on the electric quantity is analyzed, universality and fairness are achieved, and the influence degree of different factors on the electric quantity can be described more accurately.

The invention belongs to the technical field of data mining, and discloses a method and a system for calculating a maximum information coefficient between a single variable and multiple variables in abig data set, which are used for calculating the maximum information coefficient between the single variable Y and m variables (X1, X2,..., Xm). The method: searching optimal grid division of a multi-dimensional (m + 1) space; , fixing division of m variables (X1, X2,..., Xm) by utilizing a maximum information coefficient algorithm of a single variable and m-1 variables; dividing a variable Y, and finding out more suitable division for the variable Y and m variables (X1, X2,..., Xm); and calculating a normalized maximum mutual information value to serve as a maximum information coefficient value. The problem that the existing method for calculating the maximum information coefficient cannot be applied to the calculation of the maximum information coefficient between a single variable anda multivariable can be solved; according to the approximate processing method and system for the maximum information coefficient between the single variable and the multiple variables, the maximum information coefficient value between the single variable and the multiple variables can be calculated.

The invention belongs to the technical field of identification and evaluation of radiation source signals in electronic countermeasures, and discloses a radiation source signal identification efficiency evaluation method and system, an intelligent terminal and application, wherein the method comprises the steps of carrying out the incidence relation analysis of indexes through a maximum information coefficient analysis method, and obtaining an incidence relation matrix; analyzing the incidence relation matrix by adopting a rapid community detection method to obtain an optimal community division index; constructing a network evaluation model with constraint, wherein the network evaluation model adopts an Elman network structure based on sparrow search optimization; and training the network evaluation model by using a sample data set, and performing score prediction on an unknown index set by using the trained network, thereby realizing effective prediction of the evaluation score of the radiation source signal identification method. According to the method, the reverse order problem and the incomprehensive evaluation problem caused by a traditional multi-attribute decision-making method are reasonably and effectively solved, and the all-around requirements of a battlefield evaluation model for accuracy, real-time performance and intelligence can be met.