Kuzudisli, CihanBakir-Gungor, BurcuQaqish, Bahjat F.Yousef, Malik.2024-04-152024-04-152023979-835030659-0https://doi.org/10.1109/ASYU58738.2023.10296734https://hdl.handle.net/20.500.12573/2086Feature 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 SVMRCE with different variations, leading to decrease in the number of selected genes, and an increase in prediction performance.enginfo:eu-repo/semantics/closedAccessRecursive Cluster EliminationFeature SelectionClusteringGene Expression Data AnalysisconferenceObject14