Open Access

ARTICLE

A Feature Selection Method Based on Hybrid Dung Beetle Optimization Algorithm and Slap Swarm Algorithm

by Wei Liu^*, Tengteng Ren

School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110158, China

* Corresponding Author: Wei Liu. Email:

Computers, Materials & Continua 2024, 80(2), 2979-3000. https://doi.org/10.32604/cmc.2024.053627

Received 06 May 2024; Accepted 11 July 2024; Issue published 15 August 2024

Abstract

Feature Selection (FS) is a key pre-processing step in pattern recognition and data mining tasks, which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models. In recent years, meta-heuristic algorithms have been widely used in FS problems, so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization (HBCSSDBO) algorithm is proposed in this paper to improve the effect of FS. In this hybrid algorithm, the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem. By combining the K nearest neighbor (KNN) classifier, the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI (University of California, Irvine) datasets. Seven evaluation metrics such as average adaptation, average prediction accuracy, and average running time are chosen to judge and compare the algorithms. The selected dataset is also discussed by categorizing it into three dimensions: high, medium, and low dimensions. Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy, shows better optimization performance. In addition, the results of statistical tests confirm the significant validity of the method.

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Keywords

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