Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism

Feature selection is an important preprocessing task in classification that eliminates the irrelevant, redundant, and noisy features. Improving the performance of model, decreasing the computational cost, and adjusting the “curse of dimensionality” are the key advantages of feature selection task. The evolution process of the existing multi-objective based feature selection algorithms is relied on the objective space while the problem space contains useful information. This paper proposes a multi-objective PSO based method named RFPSOFS that ranks the features based on their frequencies in the archive set. Then, these ranks are used to refine the archive set and guide the particles. The proposed method is compared with three PSO based and one genetic based multi-objective methods on 9 Benchmark datasets. Qualitative and quantitative analyses of the results are performed by visual analysis of the Pareto fronts and three performance metrics respectively. Finally, remarkable performance in datasets with more than hundred features and satisfactory performance in datasets with less than hundred features are obtained.