Improved machine learning technique for feature reduction and its application in spam email detection

Abstract

This paper introduces MPAG, a new feature selection method aimed at overcoming the limitations of the conventional Marine Predators Algorithm (MPA). The MPA may experience stagnation and become trapped in local optima during optimization. To address this challenge, we propose a refined version of the MPA, termed MPAG, which incorporates the Local Escape Operator (LEO) from the gradient-based optimizer (GBO). By leveraging the LEO operator, MPAG enhances the exploration ability of the MPA, particularly during the initial one-third of iterations. This enhancement injects more diversity into populations, thereby improving the process of search space discovery and mitigating the risk of premature convergence. The performance of MPAG is evaluated on 14 feature selection benchmark datasets, employing seven performance measures including fitness value, classification accuracy, and selected features. Our findings indicate that MPAG outperforms other algorithms in 86% of the datasets, underscoring its capability to select the most relevant features across various datasets while maintaining stability. Additionally, MPAG is evaluated using two cybersecurity applications, specifically spam detection datasets, where it demonstrates superior performance across most performance measures compared to other methods.