Feature selection method and system, computer readable storage medium and electronic equipment

Abstract

The invention relates to a feature selection method and system, a computer readable storage medium and electronic equipment. The method comprises the steps that a first feature subset is acquired, anda first evaluation index corresponding to the first feature subset is determined; Obtaining a second feature subset and a third feature subset; Determining a second evaluation index corresponding tothe third feature subset; When the second evaluation index is superior to the first evaluation index, executing an updating operation; And when it is determined that the first updating cut-off condition is met, determining the current target feature subset as the target input feature of the target model, otherwise, determining the third feature subset as a new first feature subset, determining thesecond evaluation index as a new first evaluation index, and returning to the step of obtaining the second feature subset and the third feature subset. Therefore, the target input features of the better target model can be quickly and effectively screened out, the dimension of the feature set is greatly reduced, the problem of 'dimension disaster' is solved to a great extent, and the calculationefficiency is improved.

Description

technical field [0001] The present disclosure relates to the field of machine learning, and in particular, relates to a feature selection method, system, computer-readable storage medium, and electronic equipment. Background technique [0002] In machine learning, there are often many features of training samples, which can easily lead to the disaster of dimensionality. That is, when the feature dimension exceeds a certain scale, the performance of the training model decreases with the increase of the feature dimension, and the higher the dimensionality, the more time it takes to train the model. The greater the cost. Among them, the reason for the performance degradation of the training model is often because these high-dimensional features contain irrelevant and redundant features. Therefore, feature selection is used to remove irrelevant and redundant features in the features. Especially in automated machine learning, in order to train a model with better results, featur...

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Problems solved by technology

[0002] In machine learning, there are often many features of training samples, which can easily lead to the disaster of dimensionality. That is, when the feature dimension exceeds a certain scale, the performance of the training model decreases with the increase of the feature dimension, and the higher the dimensionality, the more time it takes to train the model. more expensive

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