2012 | OriginalPaper | Buchkapitel
A Particle Swarm Optimisation Based Multi-objective Filter Approach to Feature Selection for Classification
verfasst von : Bing Xue, Liam Cervante, Lin Shang, Mengjie Zhang
Erschienen in: PRICAI 2012: Trends in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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Feature selection (FS) has two main objectives of minimising the number of features and maximising the classification performance. Based on binary particle swarm optimisation (BPSO), we develop a multi-objective FS framework for classification, which is
NSBPSO
based on multi-objective BPSO using the idea of non-dominated sorting. Two multi-objective FS algorithms are then developed by applying mutual information and entropy as two different filter evaluation criteria in the proposed framework. The two proposed multi-objective algorithms are examined and compared with two single objective FS methods on six benchmark datasets. A decision tree is employed to evaluate the classification accuracy. Experimental results show that the proposed multi-objective algorithms can automatically evolve a set of non-dominated solutions to reduce the number of features and improve the classification performance. Regardless of the evaluation criteria, NSBPSO achieves higher classification performance than the single objective algorithms. NSBPSO with entropy achieves better results than all other methods. This work represents the first study on multi-objective BPSO for filter FS in classification problems.