A high-dimensional Feature Selection Based on Modified Energy Valley Optimizer and ReliefF algorithm

Abstract

A high-dimensional feature selection represents a crucial preprocessing phase in data mining and machine learning applications, exerting substantial influence on the effectiveness of machine learning algorithms. The primary goal of FS involves removing irrelevant attributes, minimizing computational time and memory demands, and improving the overall efficacy of the associated learning algorithm. The Energy Valley Optimizer (EVO) constitutes an innovative metaheuristic approach grounded in sophisticated physics concepts, specifically those connected to particle equilibrium and decomposition patterns. This research introduces an improved binary variant of The Energy Valley Optimizer (IBEVO) designed to tackle a high-dimensional feature selection challenges. The base EVO algorithm has been augmented with significant enhancement to boost its comprehensive effectiveness. ReliefF algorithm represents an addition incorporated into the EVO's initialization phase to strengthen the algorithm's capacity to utilize its potential in addition to, it integrated into the base EVO, accelerating convergence rates. The findings from ten gene expression datasets characterized by high dimensionality and limited sample sizes show that the newly developed method enhances predictive performance while simultaneously decreasing feature count, achieving highly competitive outcomes when compared to other state-of-the-art feature selection approaches.