New efficient initialization and updating mechanisms in PSO for feature selection and classification

Feature selection is one of the important and difficult issues in classification. Particle swarm optimization (PSO) is an efficient evolutionary computing technique that has been widely used to deal with feature selection problem. However, it has been observed that the traditional initialization and personal best and global best updating mechanisms in PSO often limit its performance for feature selection and has to be further explored to see the full potential of PSO for the same. This paper proposes two new efficient initialization and updating mechanisms in PSO with the goal of minimizing the number of features and maximizing the classification performance in less computational time. The proposed algorithms are compared with six existing feature selection methods, including two traditional PSO-based feature selection methods and four PSO with different initialization strategy and updating mechanism-based feature selection methods. Experiments on eight benchmark dataset show that the proposed algorithms can automatically evolve a feature subset with a smaller number of features with higher classification performance than using all features. The proposed algorithms also outperform the eight existing feature selection algorithms in terms of the classification accuracy, the number of features, and the computational cost.