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Feature selection is a preprocessing technique with great importance in the fields of data analysis, information retrieval processing, pattern classification, and data mining applications. It process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. This paper presents a novel optimization algorithm called Complementary Binary Particle Swarm Optimization (CBPSO), in which using complementary distribution strategy to improve the search capability of Binary Particle Swarm Optimization (BPSO) by facilitates global exploration and local exploitation via complementary particles and original particles, respectively.
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