A metaheuristic multi-objective interaction-aware feature selection method

TL;DR

This paper introduces a novel multi-objective feature selection method that considers feature interactions and improves exploration efficiency, leading to better Pareto optimal solutions in pattern recognition tasks.

Contribution

It proposes an advanced probability scheme and an improved PAES algorithm to effectively handle feature interactions and optimize feature subset size.

Findings

Significant improvement in Pareto front approximation over state-of-the-art methods

Effective handling of feature interactions improves classification performance

Faster convergence in exploring the solution space

Abstract

Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a better Pareto optimal solution, metaheuristic optimization methods are widely used in many studies. However, the main drawback is the exploration of a large search space. Another problem with multi-objective feature selection approaches is the interaction between features. Selecting correlated features has negative effect on classification performance. To tackle these problems, we present a novel multi-objective feature selection method that has several advantages. Firstly, it considers the interaction between features using an advanced probability scheme. Secondly, it is based on the Pareto Archived Evolution Strategy (PAES) method that has several advantages such as simplicity and its speed in exploring the solution space. However, we improve the structure of PAES in such a way that generates the offsprings, intelligently. Thus, the proposed method utilizes the introduced probability scheme to produce more promising offsprings. Finally, it is equipped with a novel strategy that guides it to find the optimum number of features through the process of evolution. The experimental results show a significant improvement in finding the optimal Pareto front compared to state-of-the-art methods on different real-world datasets.