Fractional Chebyshev Transformation for Improved Binarization in the Energy Valley Optimizer for Feature Selection

Abstract

The feature selection (FS) procedure is a critical preprocessing step in data mining and machine learning, aiming to enhance model performance by eliminating redundant features and reducing dimensionality. The Energy Valley Optimizer (EVO), inspired by particle physics concepts of stability and decay, offers a novel metaheuristic approach. This study introduces an enhanced binary version of EVO, termed Improved Binarization in the Energy Valley Optimizer with Fractional Chebyshev Transformation (IBEVO-FC), and specifically designed for feature selection challenges. IBEVO-FC incorporates several key advancements over the original EVO. Firstly, it employs a novel fractional Chebyshev transformation function to effectively map the continuous search space of EVO to the binary domain required for feature selection, leveraging the unique properties of fractional orthogonal polynomials for improved binarization. Secondly, the Laplace crossover method is integrated into the initialization phase to improve population diversity and local search capabilities. Thirdly, a random replacement strategy is applied to enhance exploitation and mitigate premature convergence. The efficacy of IBEVO-FC is rigorously evaluated on 26 benchmark datasets from the UCI Repository and compared against 7 contemporary wrapper-based feature selection algorithms. Statistical analysis confirms the competitive performance of the proposed IBEVO-FC method in terms of classification accuracy and feature subset size.