Feature selection is a task of choosing the best combination of potential features that\nbest describes the target concept during a classification process. However, selecting such relevant\nfeatures becomes a difficult matter when large number of features are involved. Therefore, this\nstudy aims to solve the feature selection problem using binary particle swarm optimization (BPSO).\nNevertheless, BPSO has limitations of premature convergence and the setting of inertia weight.\nHence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy\n(CBPSO-MIWS) is proposed in this work. The proposed method is validated with ten benchmark\ndatasets from UCI machine learning repository. To examine the effectiveness of proposed method,\nfour recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary\ngravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are\nused in a performance comparison. Our results show that CBPSO-MIWS can achieve competitive\nperformance in feature selection, which is appropriate for application in engineering, rehabilitation\nand clinical areas.
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