Multi-Objective Wrapper Differential Evolution with Guided Initial Population for Feature Selection

Feature selection is a frequent task in machine learning problems. Typically, many features are available, but only a subset is relevant, while others can be redundant or even destructive to classification accuracy. Therefore, removing these additional features is essential to reduce the dimensionality, improve classification accuracy and select discriminating features for the treated problem is essential. Filter feature selection methods use statistical tests to evaluate the “relevance” of features and are usually fast, while wrapper methods require a learning algorithm to evaluate a subset of features. Consequently, they are slower than filter methods. Naturally, the choice of the feature subset plays a crucial role, but testing all combinations for problems containing many features is impractical. Therefore, feature selection can be treated as an optimization problem to select the best feature subset that maximizes the learning algorithm's accuracy. Meta-heuristic algorithms have achieved great attention in solving innumerable optimization problems, including feature selection. Here we propose a new wrapper feature selection method that explores filter-based feature ranking algorithms, Differential Evolution, and multi-objective optimization. Experimental results show that the proposed method improved the base classifiers' performance with fewer selected features and outperformed the state-of-art algorithms for almost all evaluated datasets.