Yousef, MalikBulut, NurtenGungor, Burcu BakirQaqish, Bahjat F.2025-09-252025-09-2520249781927877913978192787768597819908000859781990800252978199080042997819278776472562-7767https://doi.org/10.11159/icsta24.144https://hdl.handle.net/20.500.12573/3771The complexity and high dimensionality of gene expression data pose significant challenges for effective feature selection and accurate classification in bioinformatics. This study introduces two novel algorithms, Support Vector Machine-Recursive Cluster Elimination (SVM-RCE) and its advanced version, SVM-RCE with Center Weights (SVM-RCE-CW), designed to optimize feature selection by leveraging clustering techniques and machine learning models. Both algorithms aim to reduce the feature space, thereby enhancing the interpretability and performance of classification models. We present a comprehensive comparison of these methods against traditional feature selection techniques, demonstrating their efficacy in achieving significant dimensionality reduction while maintaining or improving classification accuracy in several gene expression datasets. © 2024 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessClusteringFeature SelectionGene Expression Data AnalysisRecursive Cluster EliminationConference Object10.11159/icsta24.1442-s2.0-85205737356