Title: Research on the ensemble feature selection algorithm based on multimodal optimisation techniques

Authors: Yan-li Wang; Bo-yang Qu; Jing Liang; Yi Hu; Yun-peng Wei

Addresses: School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China ' School of Electric and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China

Abstract: Feature selection is essentially a high-dimensional combinatorial optimisation problem. To find representative feature subsets, the selection method needs powerful exploration ability. In addition, if alternative feature subsets could be provided, the final prediction accuracy can be improved by ensembling these subsets. Multimodal optimisation (MO) methods with high exploration power can find multiple suitable solutions in one single run. Therefore, this paper presents an ensemble feature selection algorithm based on multimodal optimisation techniques. Differential evolution based on fitness Euclidean-distance ratio (FERDE) algorithm is utilised to search for multiple diverse feature subsets in the huge feature space. A set of diverse-based classifiers are built based on these subsets and ensemble to improve the final classification performance. Compared with several existing classical algorithms and ensemble feature selection methods, the proposed method can achieve higher predictive accuracy.

Keywords: feature selection; ensemble learning; multimodal optimisation; classification.

DOI: 10.1504/IJBIC.2021.117425

International Journal of Bio-Inspired Computation, 2021 Vol.18 No.1, pp.49 - 58

Received: 25 Sep 2019
Accepted: 02 Aug 2020
Published online: 06 Sep 2021 *

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