Abstract Feature selection is one of important data processing methods in pattern classification. A method of selecting features is proposed based on knowledge guided particle swarm optimization. The problem of feature selection which contains discrete variables is converted to an optimization one with continuous variables by encoding the particles with the selected probabilities of features. The type and its updated probability of the feature are determined by the particle′s fitness and the selection frequency of the particle component in order to speed up the convergence of the swarm. The experimental results on 10 typical test datasets and a clinical diagnosis dataset of hepatitis show that the proposed method improves the classification accuracy on the premise of reducing the number of features.
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2014, Vol. 27 
