Grey wolf optimization algorithm gene selection method based on diffusion and chaotic local search

Abstract

The invention provides a grey wolf optimization algorithm gene selection method based on diffusion and chaotic local search, which comprises the following steps of generating a training set and a testset according to a gene data set acquired from a public website, performing global search on the training set and the test set of the gene data set by using a preset grey wolf optimization algorithm,and determining a feature subset of the gene data set by combining fitness functions defined by the training set and the test set in the preset grey wolf optimization algorithm based on a KNN classifier, selecting a globally optimal solution from the determined feature subset of the gene data set by using a preset diffusion strategy, and further performing chaotic local search on the selected globally optimal solution to obtain an optimal training set and an optimal test set of the gene data set as final optimal gene subsets, and outputting the finally obtained optimal gene subset. By implementing the method, the problem of low gene data classification precision of the gene subset obtained in the prior art can be effectively solved, and the optimal gene subset is found.

Description

technical field [0001] The invention relates to the technical field of gene selection, in particular to a gene selection method and system based on a gray wolf optimization algorithm based on diffusion and chaotic local search. Background technique [0002] Gene expression profiles generated by microarray technology provide insights for clinical decision-making. However, due to the complexity and large-scale nature of gene expression data, which contains irrelevant and redundant information, it brings great challenges to identify representative genes and reduce high dimensionality. [0003] Aiming at problems such as low computational efficiency, overfitting, and performance degradation of machine learning techniques in the analysis of gene expression data, an important data preprocessing technique, gene selection (GS), aims to Through the weakening of the gene dimension, irrelevant and useless genes are eliminated, and discriminative genes are identified in the intricate g...

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Problems solved by technology

[0007] The technical problem to be solved by the embodiments of the present invention is to provide a gene selection method and system based on the gray wolf optimization algorithm of diffusion and chaotic local search, which can effectively overcome the low classification accuracy of gene subsets obtained in the prior art for gene data The problem of finding the optimal gene subset while maintaining high classification accuracy of genetic data

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