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Business object classification method and device, equipment and storage medium

A technology of business objects and classification methods, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of single processing method and low classification accuracy

Pending Publication Date: 2022-05-31
BIGO TECH PTE LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The average value method has a single processing method for each score vector, and uses a unified processing method for different business objects, and the classification accuracy is low

Method used

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  • Business object classification method and device, equipment and storage medium
  • Business object classification method and device, equipment and storage medium
  • Business object classification method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] figure 1 It is a flow chart of a business object classification method provided by Embodiment 1 of the present application. This embodiment is applicable to the situation where the result of multiple classifications is integrated into the final classification result through an integration mechanism based on node degree. This method can be composed of The apparatus for classifying business objects can be implemented in the form of hardware and / or software, and the apparatus for classifying business objects can be configured in the apparatus for classifying business objects. Such as figure 1 As shown, the method includes:

[0036] Step 101, converting the business object into a graph neural network.

[0037] In different business scenarios, there are different business objects, which are collections of data with characteristics of the business domain.

[0038] For example, for the user-oriented service field, the business object can be users; for the news media field, ...

Embodiment 2

[0116] image 3 It is a schematic structural diagram of an apparatus for classifying business objects provided in Embodiment 2 of the present application. Such as image 3 As shown, the device includes:

[0117] The graph neural network conversion module 301 is used to convert the business object into a graph neural network, the business object has multiple feature vectors, and the nodes in the graph neural network represent multiple feature vectors of the business object;

[0118] A node degree calculation module 302, configured to calculate the degree of association between the nodes in the graph neural network as the node degree of the nodes;

[0119] The first classification module 303 is configured to execute the graph neural network to output a first probability that the business object belongs to a preset category;

[0120]The second classification module 304 is configured to execute a preset classification model to identify a second probability that the business obj...

Embodiment 3

[0162] Figure 4 A schematic structural diagram of a classification device 10 that can be used to implement the business object of the embodiment of the present application is shown.

[0163] Such as Figure 4 As shown, the business object classification device 10 includes at least one processor 11, and a memory connected in communication with the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores There is a computer program executable by at least one processor, and the processor 11 can operate according to a computer program stored in a read-only memory (ROM) 12 or loaded from a storage unit 18 into a random access memory (RAM) 13. Various appropriate actions and processes are performed. In the RAM 13, various programs and data necessary for the operation of the classification apparatus 10 of business objects may also be stored. The processor 11 , ROM 12 , and RAM 13 are connected to each other t...

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PUM

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Abstract

The invention discloses a business object classification method and device, equipment and a storage medium, and the method comprises the steps: converting a business object into a graph neural network, the business object having a plurality of feature data, and nodes in the graph neural network representing a plurality of feature vectors of the business object; calculating the correlation degree between the nodes in the graph neural network as the node degree of the nodes; executing the graph neural network to output a first probability that the business object belongs to a preset category; identifying a second probability that the business object belongs to a preset category by using the feature vector; for the same category, fusing the first probability and the second probability into a third probability according to the node degree; and determining the category to which the business object belongs according to the third probability. According to the embodiment of the invention, classification is finally determined by using node degree organic combination graph neural network to classify community node prediction and classify single node prediction based on feature vectors, and the classification precision is improved.

Description

technical field [0001] The present application relates to the technical field of computer processing, and in particular to a business object classification method, device, equipment and storage medium. Background technique [0002] In business scenarios such as community mining and anomaly detection, business objects such as users, videos, and audios are classified, and different classification algorithms have different advantages and disadvantages. Therefore, multiple classification algorithms are often used for classification, and the integration mechanism will The results of multiple classifications are integrated into a final classification result. [0003] At present, the integration mechanism mostly uses the average value method, that is, multiple classification algorithms output multiple score vectors for a business object, and calculate the average value of all score vectors, which is the final score vector. The category corresponding to the largest score vector is ...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06N3/04G06N3/08
CPCG06F16/2465G06N3/08G06N3/045
Inventor 李岩
Owner BIGO TECH PTE LTD