Feature selection based on brain storm optimization for data classification

Pourpanah, Farhad1; Shi, Yuhui2; Lim, Chee Peng3; Hao, Qi2; Tan, Choo Jun4

2019-07

10.1016/j.asoc.2019.04.037

APPLIED SOFT COMPUTING   影响因子和分区

1568-4946

1872-9681

Brain storm optimization (BSO) is a new and effective swarm intelligence method inspired by the human brainstorming process. This paper presents a novel BSO-based feature selection technique for data classification. Specifically, the Fuzzy ARTMAP (FAM) model, which is employed as an incremental learning neural network, is combined with BSO, which acts as a feature selection method, to produce the hybrid FAM-BSO model for feature selection and optimization. Firstly, FAM is used to create a number of prototype nodes incrementally. Then, BSO is used to search and select an optimal sub-set of features that is able to produce high accuracy with the minimum number of features. Ten benchmark problems and a real-world case study are employed to evaluate the performance of FAM-BSO. The results are quantified statistically using the bootstrap method with the 95% confidence intervals. The outcome indicates that FAM-BSO is able to produce promising results as compared with those from original FAM and other feature selection methods including particle swarm optimization, genetic algorithm, genetic programming, and ant colony optimization. (C) 2019 Elsevier B.V. All rights reserved.

Feature selection Brain storm optimization Fuzzy ARTMAP Data classification

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 ; Computer Science, Interdisciplinary Applications

WOS:000470661600056

ELSEVIER SCIENCE BV

20192006923594

Ant colony optimization ; Artificial intelligence ; Benchmarking ; Classification (of information) ; Genetic algorithms ; Genetic programming ; Particle swarm optimization (PSO) ; Statistical methods ; Storms

Precipitation:443.3 ; Information Theory and Signal Processing:716.1 ; Computer Programming:723.1 ; Artificial Intelligence:723.4 ; Optimization Techniques:921.5 ; Mathematical Statistics:922.2

COMPUTER SCIENCE

Web of Science

1.Shenzhen Univ, Coll Math & Stat, Shenzhen, Guangdong, Peoples R China
2.Southern Univ Sci & Technol, Sch Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China
3.Deakin Univ, Inst Intelligent Syst Res & Innovat, Geelong, Vic, Australia
4.Wawasan Open Univ, Sch Sci & Technol, George Town, Pulau Pinang, Malaysia

Pourpanah, Farhad,Shi, Yuhui,Lim, Chee Peng,et al. Feature selection based on brain storm optimization for data classification[J]. APPLIED SOFT COMPUTING,2019,80:761-775.

Pourpanah, Farhad,Shi, Yuhui,Lim, Chee Peng,Hao, Qi,&Tan, Choo Jun.(2019).Feature selection based on brain storm optimization for data classification.APPLIED SOFT COMPUTING,80,761-775.

Pourpanah, Farhad,et al."Feature selection based on brain storm optimization for data classification".APPLIED SOFT COMPUTING 80(2019):761-775.