MHGSO: A Modified Hunger Game Search Optimizer Using Opposition-Based Learning for Feature Selection

Abstract

Feature selection (FS) is one of the crucial pre-processing tasks in many data mining, machine learning, and pattern recognition applications. It facilitates limiting the feature count, dimensionality of datasets, and overfitting. Feature selection methods are developed to explore the benefits with good accuracy outcomes. This paper proposes a novel modification to the conventional Hunger Games Search optimization using the concept of opposition-based learning (OBL) to solve the FS problem. Here, the opposition-based learning enables the searching ability of HGSO to determine the optimal solution by looking in random directions and in the opposite directions as well, simultaneously. Moreover, three binarization approaches, namely transfer function (TF), great value priority (GVP), and angle modulation (AM), have been incorporated with modified HGSO and investigated to study the effective feature selection ability of the modified optimizer. The simulation results have been obtained using standard datasets for accuracy, fitness value, and a number of features, along with a convergence curve for each dataset. The obtained results demonstrate better performance compared to the Support Vector Machine (SVM) approach over most of the datasets for effective feature selection.