Incorporation of multimodal multiobjective optimization in designing a filter based feature selection technique

Current work reports about the development of a new feature selection technique fusing two concepts, multimodal multiobjective optimization and filter-based feature selection. Use of the concept of multimodality in multiobjective based feature selection helps in generating diverse set of feature subsets on final Pareto front. This approach evaluates the quality of the reduced feature set by utilizing different quality measures. This process of feature selection focuses on achieving two discrete objectives: (1) identification of a large number of Pareto-optimal solutions along with achieving a good distribution in both objective and decision spaces; (2) making a selection of feature subset with minimal redundancy and high correlation with classes. To achieve the second objective, a variety of objective functions based on information-theoretic measures like normalized mutual information and correlation with class attributes are utilized. A multiobjective ring-based Particle swarm Optimization (PSO) and non-dominated sorting with special crowding distance are employed to cover the aspects corresponding to the first objective. An evaluation is carried out on seven publicly available datasets concerning different classifiers. The results of these experiments illustrate that the multimodal PSO based feature selection approach finds more feature subsets than its simple PSO counterpart in multiobjective environment. And, the results are also compared with those of existing wrapper based multimodal multiobjective feature selection methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The work introduces a new feature selection technique that combines multimodal multiobjective optimization and filter-based feature selection, aiming to generate diverse feature subsets and evaluate their quality using different measurements. By employing multiobjective PSO and non-dominated sorting with special crowding distance, the approach achieves the objectives of identifying a large number of Pareto-optimal solutions and selecting feature subsets with minimal redundancy and high correlation. Experimental results demonstrate that the multimodal PSO based feature selection approach outperforms its simple PSO counterpart in finding more feature subsets in multiobjective environment.