Chaotic Binary Particle Swarm with Anti Stagnation Strategy on Feature Selection
| Autor: | Nadjette Dendani, Hayet Djellali |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Computer Science::Neural and Evolutionary Computation MathematicsofComputing_NUMERICALANALYSIS Chaotic Chaotic map Particle swarm optimization Binary number Feature selection 02 engineering and technology ComputingMethodologies_ARTIFICIALINTELLIGENCE Term (time) 020901 industrial engineering & automation Local optimum Chaotic systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Advances in Computing Systems and Applications ISBN: 9783030694173 CSA |
| DOI: | 10.1007/978-3-030-69418-0_14 |
| Popis: | This paper investigates Chaotic Restart Binary Particle Swarm PSO (ChResBPSO) algorithm applied to feature selection. In this study, to escape local optima of particle swarm PSO and solve the stagnation problem, we add new particles using prior information about current global best and its neighborhood. The solution adopted is to update the particles using N-Best previous particles, their neighbors and new random particles. The information on worst features is incorporated to direct the novel solutions to avoid them. Various chaotic systems replace the main parameters of PSO to find the best chaotic map. Experiments conducted on UCI data: hepatitis, breast cancer, colon cancer, DLBCL validate that chaotic PSO with anti-stagnation criterion outperforms the state of the art methods (BPSO), chaotic BPSO, artificial bee colony (ABC). The novel ChResBPSO enhances the final solution in term of accuracy and minimal number of features. |
| Databáze: | OpenAIRE |
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