Comparison of the Computational time of An Improved Quasi Equally Informative Subsets in Feature Selection

This paper presents an enhanced feature selection method, the Improved Quasi Equally Informative Subsets in Feature Selection (QEISS), which addresses the computational inefficiencies of traditional approaches. By integrating Extreme Learning Machines (ELM) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the improved method achieves faster execution times without sacrificing accuracy. The study introduces both a filter-based and wrapper-based QEISS variant, demonstrating consistent reductions in computational time across various high-dimensional datasets from the UCI repository. The method provides a balance between feature relevance, redundancy, subset size, and classification accuracy, marking a significant advancement in multi-objective feature selection.