Feature selection method for high-dimensional data

Abstract

The invention discloses a feature selection method for high-dimensional data, and relates to a feature selection method. The invention provides a feature selection method for high-dimensional data, which can efficiently and stably screen features. The method comprises the following specific steps: 1) stability scoring of features; 2) selecting a feature subset; 3) evaluating the feature subset; and 4) verifying the effectiveness of the feature subsets, namely verifying the selected feature subsets through different classifiers, indicating that the selected feature subsets have generalization and representativeness on the different classifiers, and further indicating the effectiveness of the feature selection method. A new feature selection method oriented to high-dimensional data is provided, and a better feature subset can be obtained by combining the idea of a wepper and the idea of an embedded. In combination with a greedy strategy, the step length of search can be customized, the feature dimension can be well determined, and the feature selection process can be terminated in time.

Description

technical field [0001] The invention relates to a feature selection method, in particular to a feature selection method oriented to high-dimensional data, which can efficiently and stably screen features. Background technique [0002] Feature selection is very important for the classification of high-dimensional data. It selects some of the most important features from a set of features to reduce the dimensionality of the feature space. [1] . The quality of the feature selection results directly affects the accuracy of the classification results. Feature selection methods in the field of bioinformatics [2-4] , image field [5-7] and text fields [8-10] etc. have a wide range of applications. Feature selection generally consists of four steps: feature subset search process, feature subset evaluation method, feature subset search stopping criterion, and feature subset validity verification. [11] . Commonly used feature selection methods include filter, wrapper and embedde...

AI Technical Summary

Problems solved by technology

The filter method can achieve fast feature selection, but it is difficult to obtain a high accuracy rate; the wrapper method can obtain a high accuracy rate, but the calculation cost is large, and it is not easy to promote [12]

The Embedded method scores the features based on the classification algorithm, and then implements feature selection, but the dimension of the features cannot be well determined

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