New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm

Abstract

Feature selection is crucial for minimizing redundancy in information and addressing the limitations of traditional classification methods when dealing with large datasets and numerous features in many machine learning applications. To improve the classification, this article introduced two hybrid methods utilizing a genetic algorithm and a gray wolf algorithm with structured dispersion norms for feature selection. These techniques involved the utilization of a genetic algorithm and a gray wolf algorithm for feature selection. The features selected by these algorithms were used in the classification process by employing a two-layer perceptron as a classifier. The novel sparse norm is employed to assess and compute classification errors in these methodologies. To assess the effectiveness of the suggested techniques, they were compared with the existing feature selection methods using various publicly accessible datasets. The results of the experiments consistently demonstrate that the proposed methods outperform other approaches.