Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An introduction to variable and feature selection
The Journal of Machine Learning Research
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Improved binary PSO for feature selection using gene expression data
Computational Biology and Chemistry
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Evolutionary Rough Feature Selection in Gene Expression Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
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In classification, feature selection is an important, but difficult problem. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes a new initialisation strategy and a new personal best and global best updating mechanism in PSO to develop a novel feature selection algorithm with the goals of minimising the number of features, maximising the classification performance and simultaneously reducing the computational time. The proposed algorithm is compared with two traditional feature selection methods, a PSO based method with the goal of only maximising the classification performance, and a PSO based two-stage algorithm considering both the number of features and the classification performance. Experiments on eight benchmark datasets show that the proposed algorithm can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features. The proposed algorithm achieves significantly better classification performance than the two traditional methods. The proposed algorithm also outperforms the two PSO based feature selection algorithms in terms of the classification performance, the number of features and the computational cost.