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

Pourpanan, Farhad1; Lim, Chee Peng2; Wang, Xizhao3; Tan, Choo Jun4; Seera, Manjeevan5; Shi, Yuhui6

2019-03-14

10.1016/j.neucom.2019.01.011

NEUROCOMPUTING   影响因子和分区

0925-2312

1872-8286

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. (C) 2019 Elsevier B.V. All rights reserved.

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

SCI ; EI

英语

其他

Natural Science Foundation of Shenzhen University[827-000140] ; Natural Science Foundation of Shenzhen University[827-000230] ; Natural Science Foundation of Shenzhen University[2017060]

Computer Science

Computer Science, Artificial Intelligence

WOS:000456834100040

ELSEVIER SCIENCE BV

20190306396938

Fault detection ; Feature extraction ; Genetic algorithms ; Particle swarm optimization (PSO) ; Statistical methods ; Storms

Precipitation:443.3 ; Information Theory and Signal Processing:716.1 ; Computer Software, Data Handling and Applications:723 ; Artificial Intelligence:723.4 ; Mathematical Statistics:922.2

COMPUTER SCIENCE

Web of Science

1.Shenzhen Univ, Coll Math & Stat, Shenzhen, Guangdong, Peoples R China
2.Deakin Univ, Inst Intelligent Syst Res & Innovat, Geelong, Vic, Australia
3.Shenzhen Univ, Guang Dong Key Lab Intelligent Informat Proc, Coll Comp Sci & Software Engn, Shenzhen, Guangdong, Peoples R China
4.Wawasan Open Univ, Sch Sci & Technol, George Town, Penang, Malaysia
5.Swinburne Univ Technol, Fac Engn Comp & Sci, Sarawak Campus, Kuching, Sarawak, Malaysia
6.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China

Pourpanan, Farhad,Lim, Chee Peng,Wang, Xizhao,et al. A hybrid model of fuzzy min-max and brain storm optimization for feature selection and data classification[J]. NEUROCOMPUTING,2019,333:440-451.

Pourpanan, Farhad,Lim, Chee Peng,Wang, Xizhao,Tan, Choo Jun,Seera, Manjeevan,&Shi, Yuhui.(2019).A hybrid model of fuzzy min-max and brain storm optimization for feature selection and data classification.NEUROCOMPUTING,333,440-451.

Pourpanan, Farhad,et al."A hybrid model of fuzzy min-max and brain storm optimization for feature selection and data classification".NEUROCOMPUTING 333(2019):440-451.