Abstract
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.
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Djellali, H., Dendani, N. (2021). Chaotic Binary Particle Swarm with Anti Stagnation Strategy on Feature Selection. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds) Advances in Computing Systems and Applications. CSA 2020. Lecture Notes in Networks and Systems, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-69418-0_14
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