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Feature selection method and device for model training and electronic equipment

A feature selection method and model training technology, applied in character and pattern recognition, instrumentation, and other database retrieval, can solve problems such as difficulties and lack of physical meaning, reduce feature dimensions, reduce overfitting, and improve training accuracy Effect

Pending Publication Date: 2020-05-05
BEIJING KINGSOFT CLOUD NETWORK TECH CO LTD
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Problems solved by technology

However, the features after feature extraction are no longer the original features, and there is no physical meaning of the original features, which causes great difficulties in understanding the research problems.

Method used

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  • Feature selection method and device for model training and electronic equipment
  • Feature selection method and device for model training and electronic equipment
  • Feature selection method and device for model training and electronic equipment

Examples

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Embodiment Construction

[0050] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0051] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses.

[0052] Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of the description.

[0053] In all examples shown and discussed herein, any specific values ​​should be construed as exemplary only, and not as limitations. Therefore, other instances of the exemplary embodiment may have dif...

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Abstract

The invention discloses a feature selection method and device for model training, electronic equipment and a computer readable storage medium. The method comprises the steps:calculating the mutual information amount of to-be-selected features and labels in an original feature set to obtain the correlation degree between each to-be-selected feature and the corresponding label; calculating the average mutual information amount of the to-be-selected features and the selected feature subsets of the original feature set to obtain the redundancy of each to-be-selected feature and the selected feature subset; and selecting a feature subset from the to-be-selected features as input data for model training according to at least one of the correlation degree and the redundancy. According to the invention, the model training time can be reduced, and the training precision is improved.

Description

technical field [0001] The invention relates to the field of software technology, in particular to a feature selection method and device for model training. Background technique [0002] In the cloud storage service, all kinds of files uploaded by users must be checked for compliance to determine whether the files are related to politics, terrorism, pornography, etc. The golden eye content recognition service is based on this. In the final analysis, these are actually problems of document classification, where documents include text, images, audio and video, and so on. Classification problems can use traditional machine learning methods or depth-based methods. [0003] No matter which method is used for the existing classification technology, model training must be carried out first. In this era of big data, the process of model training always faces the so-called "dimension disaster", that is, the feature dimension of the sample is very large, resulting in training If the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/906G06K9/62
CPCG06F16/906G06F18/241
Inventor 李勇刘鹏程
Owner BEIJING KINGSOFT CLOUD NETWORK TECH CO LTD
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