Abstract
With the purpose of finding the minimal reduct, this paper proposes a novel feature selection algorithm based on artificial fish swarm algorithm (AFSA) hybrid with rough set (AFSARS). The proposed algorithm searches the minimal reduct in an efficient way to observe the change of the significance of feature subsets and the number of selected features, which is experimentally compared with the quick reduct and other hybrid rough set methods such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO) and chaotic binary particle swarm optimization (CBPSO). Experiments demonstrate that the proposed algorithm could achieve the minimal reduct more efficiently than the other methods.
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Wang, F., Xu, J., Li, L. (2014). A Novel Rough Set Reduct Algorithm to Feature Selection Based on Artificial Fish Swarm Algorithm. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_4
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DOI: https://doi.org/10.1007/978-3-319-11897-0_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11896-3
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