Minimum redundancy maximum relevance ensemble feature selection: A bi-objective Pareto-based approach

- Ensemble feature selection methods are used to improve the robustness of feature selection algorithms. These approaches are a combination of several feature selection methods to achieve the final ranking of features. The reason for using such approaches is derived from the fact that the variety of different methods is more effective than only one method. Each feature selection algorithm may find feature subsets that can be considered local optima in the feature subsets space. Ensemble feature selection is a solution to address this problem. In this paper, we have proposed a bi-objective feature selection algorithm based on Pareto-based ranking. The maximum relevancy and minimum redundancy are considered as our two objectives. Both of the objectives are obtained by the ensemble of three feature selection methods. The final evaluation of features is according to a bi-objective optimization process and the crowding distance of features in this space for ranking the features. The proposed method results are compared with recent ensemble feature selection algorithms and simple feature selection algorithms. The results show that our classification accuracy method is superior to other similar methods and performs in a short time.