Interaction feature selection method based on neighborhood condition mutual information

Abstract

The invention discloses an interactive feature selection method based on neighborhood condition mutual information. The method comprises the steps: firstly, determining the neighborhood relation of each feature through employing an HCOM distance function for different data types, and calculating a neighborhood similarity relation matrix of each feature according to a multi-neighborhood radius set; secondly, exploring relevance between the features by utilizing neighborhood information, wherein the relevance comprises relevance between the features and classes and redundancy and interactivity between the features, and based on the relevance, establishing an evaluation function of feature importance of maximum relevance, minimum redundancy and maximum interactivity (MRmRMI). scoring the importance of the features through the evaluation function to obtain an ordered feature sequence with classification contributions from large to small; and finally, selecting a final reduction feature subset through testing on different classifiers, wherein the feature subset is a feature subset sequence corresponding to the optimal average classification performance. Compared with other six popular feature selection algorithms, the method of the invention has high classification performance and a more significant classification effect.

Description

technical field [0001] The invention belongs to the technical field of data mining, and is a feature selection method for mixed data containing noise and uncertainty. The method comprehensively considers the correlation between features and classes, and the redundancy and interaction between features. Background technique [0002] In recent years, the development of big data applications has put forward higher requirements for the understanding and processing of high-dimensional data. In particular, large datasets with noisy, irrelevant or redundant features pose great challenges for data mining, knowledge discovery and pattern recognition. Due to the existence of the curse of dimensionality, how to select the optimal feature subset from all features is considered to be a worthy research topic in various learning tasks. In response to this problem, many feature selection methods have been proposed, which are dedicated to removing irrelevant features and eliminating redundan...

AI Technical Summary

Problems solved by technology

[0005] In view of the above deficiencies in the prior art, the purpose of the present invention is to re-characterize the interaction between features for mixed data containing noise and uncertainty, and perform interactive analysis on the

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