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A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification

Version 2 2024-06-06, 08:10
Version 1 2019-02-18, 13:26
journal contribution
posted on 2024-06-06, 08:10 authored by F Pourpanah, Chee Peng LimChee Peng Lim, X Wang, CJ Tan, M Seera, Y Shi
Swarm intelligence (SI)-based optimization methods have been extensively used to tackle feature selection problems. A feature selection method extracts the most significant features and removes irrelevant ones from the data set, in order to reduce feature dimensionality and improve the classification accuracy. This paper combines the incremental learning Fuzzy Min–Max (FMM) neural network and Brain Storm Optimization (BSO) to undertake feature selection and classification problems. Firstly, FMM is used to create a number of hyperboxes incrementally. BSO, which is inspired by the human brainstorming process, is then employed to search for an optimal feature subset. Ten benchmark problems and a real-world case study are conducted to evaluate the effectiveness of the proposed FMM-BSO. In addition, the bootstrap method with the 95% confidence intervals is used to quantify the results statistically. The experimental results indicate that FMM-BSO is able to produce promising results as compared with those from the original FMM network and other state-of-the-art feature selection methods such as particle swarm optimization, genetic algorithm, and ant lion optimization.

History

Journal

Neurocomputing

Volume

333

Pagination

440-451

Location

Amsterdam, The Netherlands

ISSN

0925-2312

eISSN

1872-8286

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, Elsevier B.V.

Publisher

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