Feature selection for classification using particle swarm optimization

This paper proposes a method for the problem of processing high-dimensional data. When one has thousands of features (attributes) in a dataset, it is hard to achieve an efficient feature selection. To cope with this problem, we propose the use of a binary particle swarm optimization algorithm combined with the C4.5 as a classifier in the fitness function for the selection of informative attributes. The results obtained on 11 datasets were analyzed statistically and reveal that the proposed method, called BPSO+C4.5, outperforms known classifiers, i.e., C4.5, Naive Bayes, and SVM.