Kuzudisli, CihanBakir-Güngör, BurcuQaqish, Bahjat F.Yousef, Malik2025-09-252025-09-2520239798350306590https://doi.org/10.1109/ASYU58738.2023.10296734https://hdl.handle.net/20.500.12573/3693Feature selection (FS) is an effective tool in dealing with high dimensionality and reducing computational cost. Support Vector Machines-Recursive Cluster Elimination (SVM-RCE) is one of several algorithms that have been developed for FS in high dimensional data. SVM-RCE involves a clustering step which originally is k-means. Using various performance metrics, three alternative algorithms are evaluated in this context; k-medoids, Hierarchical Clustering (HC), and Gaussian Mixture Model (GMM). Comparisons will be carried out on five publicly available gene expression datasets. The results show that k-means in SVM-RCE obtains higher performance than other tested algorithms in terms of classification performance. Additionally, HC shows a similar performance to k-means. Our findings show superiority of using k-means. This study can contribute to the development of SVM-RCE with different variations, leading to decrease in the number of selected genes, and an increase in prediction performance. © 2023 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessClusteringFeature SelectionGene Expression Data AnalysisRecursive Cluster EliminationFeature SelectionGaussian DistributionGene ExpressionK-Means ClusteringClusteringsFeatures SelectionGene Expression DataGene Expression Data AnalysisHier-Archical ClusteringHierarchical ClusteringK-MeansPerformanceRecursive Cluster EliminationSupport Vectors MachineSupport Vector MachinesConference Object10.1109/ASYU58738.2023.102967342-s2.0-85178301702