An improved Michigan particle swarm optimization for classification
作者： Pei Wu , Ruochen Liu , Jingjing Ma , Yangyang Li
关键词:
摘要: Classification is one of the most frequently occurring tasks human decision making. In this paper, two improved versions Michigan particle swarm optimization (MPSO), Improved MPSO1 (IMPSO1) and MPSO2 (IMPSO2), are proposed. IMPSO1 adopts a adaptive inertia factor so as to flexibly control search path, moreover, both nearest neighbor (NN) 5-NN classification used take more local information into account improve diversity population. IMPSO2, new selection operator introduced MPSO obtain competitive success rate well lower computation cost. The proposed algorithm has been extensively compared with PSO, MPSO, C4.5, 1-NN 3-NN over eight UCI data sets. result experiment indicates superiority other four algorithms on rate.
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