A new representation in genetic programming with hybrid feature ranking criterion for high-dimensional feature selection

Abstract

Feature selection is a common method for improving classification performance. Selecting features for high-dimensional data is challenging due to the large search space. Traditional feature ranking methods that search for top-ranked features cannot remove redundant and irrelevant features and may also ignore interrelated features. Evolutionary computation (EC) techniques are widely used in feature selection due to their global search capability. However, EC can easily fall into local optima when dealing with feature selection for high-dimensional applications. The top-ranked features are more likely to construct effective feature subsets and help EC reduce the search space. This paper proposes a feature selection method based on Genetic Programming (GP) with hybrid feature ranking criterion called GPHC, which combines multiple feature ranking methods into the GP structure using a novel GP representation to search for effective feature subsets. Experiments on eight high-dimensional datasets show that GPHC achieves significantly better classification performance compared to five feature ranking methods. Further comparisons between GPHC and other evolutionary algorithms demonstrate that GPHC has advantages in terms of classification performance, the number of features, and convergence speed.