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

Farhad Pourpanah, Chee Peng Lim, Xizhao Wang, Choo Jun Tan, Manjeevan Seera, Yuhui Shi

Research output: Contribution to journalArticleResearchpeer-review

28 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)440-451
Number of pages12
JournalNeurocomputing
Volume333
DOIs
Publication statusPublished - 14 Mar 2019
Externally publishedYes

Keywords

  • Brain storm optimization
  • Data classification
  • Feature selection
  • Fuzzy min–max
  • Motor fault detection

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