Feature selection method, device and equipment

A feature selection method and decision tree technology, applied in the field of big data, can solve the problem of low accuracy of feature selection

Inactive Publication Date: 2019-01-04
NEUSOFT CORP
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  • Claims
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Problems solved by technology

[0005] In order to solve the problem of low accuracy of feature selection through a decision tree in the prior art, the embodiment of the present application provides a feature selection method, device and equipment for improving the accuracy of feature selection

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  • Feature selection method, device and equipment
  • Feature selection method, device and equipment
  • Feature selection method, device and equipment

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Embodiment Construction

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0053] Feature selection refers to selecting some representative features from multiple features to reduce the dimensionality of features. In the prior art, a decision tree is usually used for feature selection. A decision tree is a supervised learning algorithm that can be used for classification and regression, and can also be used for screening features to be se...

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Abstract

The embodiment of the application discloses a feature selection method, a device and equipment. By acquiring a plurality of features to be selected and data corresponding to the plurality of featuresto be selected respectively, the acquired features to be selected and data corresponding to the plurality of features to be selected are input into a random forest model for training to obtain a plurality of different decision trees. According to the weights of the features to be selected in each decision tree, an important index reflecting the importance of the features to be selected is synthetically obtained, and then the features to be selected are screened according to the important index. Compared with the prior art, the embodiments of the present application do not depend on the hierarchy of the features to be selected in a single decision tree, weaken the influence of the determination of the position of the features to be selected in a single decision tree on the screening of thefeatures to be selected, and improve the screening accuracy of the features to be selected. At the same time, the technical scheme can reduce the influence of the imbalance of single decision tree tothe selection of features, and then improve the accuracy of the selection of features to be selected.

Description

technical field [0001] The present application relates to the field of big data, and in particular to a feature selection method, device and equipment. Background technique [0002] Feature Selection (FS), also known as Feature Subset Selection (FSS), or Attribute Selection (AS), refers to selecting some representative features from multiple features. , to reduce the dimensionality of features and reduce the amount of calculations for subsequent machine learning. [0003] At present, decision trees are usually used for feature selection, that is, firstly, the features to be selected and their corresponding data are input into the decision tree model, and a decision tree including multi-layer nodes is generated, where the nodes are the features to be selected, and then according to the The level of the feature in the decision tree is used to filter the features to be selected. [0004] This method of feature selection through decision trees depends on the accuracy of the po...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2113G06F18/24323
Inventor 张雷高睿苗元君
Owner NEUSOFT CORP
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