An effective hybrid model based on PSO-SVM algorithm with a new local search for feature selection

Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support vector machine (SVM) for improving classification accuracy and selecting a subset of salient feature. This optimization mechanism with combination of discrete PSO and continuous PSO simultaneously selects a subset of salient feature and tunes support vector machine parameters. In this algorithm, a new local search based on reinforcement learning is utilized for obtaining optimal feature subset. The numerical results and statistical analysis show that the proposed method performs significantly better than the other methods in terms of prediction accuracy with smaller subset of features on low and high dimensional datasets.