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Multi-objective feature selection is one of the most significant issues inthe field of pattern recognition. It is challenging because it maximizes theclassification performance and, at the same time, minimizes the number ofselected features, and the mentioned two objectives are usually conflicting. Toachieve a better Pareto optimal solution, metaheuristic optimization methodsare widely used in many studies. However, the main drawback is the explorationof a large search space. Another problem with multi-objective feature selectionapproaches is the interaction between features. Selecting correlated featureshas negative effect on classification performance. To tackle these problems, wepresent a novel multi-objective feature selection method that has severaladvantages. Firstly, it considers the interaction between features using anadvanced probability scheme. Secondly, it is based on the Pareto ArchivedEvolution Strategy (PAES) method that has several advantages such as simplicityand its speed in exploring the solution space. However, we improve thestructure of PAES in such a way that generates the offsprings, intelligently.Thus, the proposed method utilizes the introduced probability scheme to producemore promising offsprings. Finally, it is equipped with a novel strategy thatguides it to find the optimum number of features through the process ofevolution. The experimental results show a significant improvement in findingthe optimal Pareto front compared to state-of-the-art methods on differentreal-world datasets.

A metaheuristic multi-objective interaction-aware feature selection method