Medoid-Based SVM-RCE: Efficient Feature Selection for High-Dimensional Gene Expression Data Analysis

Abstract

High-dimensional gene expression data pose computational challenges for feature selection due to redundancy, noise, and limited sample sizes. Recursive Cluster Elimination (RCE) addresses these issues through iterative feature clustering and elimination. Conventional implementations of RCE require repeated cross-validation steps within each cluster that increase the computational cost. The RCE with Center Weights (RCE-CW) framework addresses this by evaluating all clusters simultaneously through a single linear model. This study extends RCE-CW by replacing synthetic cluster centroids with medoid representatives, introducing Medoid-SVM-RCE and Medoid-RF-RCE. Experiments on 17 gene expression datasets show that Medoid-based variants select approximately 46-51% fewer genes than RCE-CW and SVM-RCE, while maintaining statistically comparable classification performance. Across 17 datasets, Medoid-based variants completed all experiments within 9 h, whereas SVM-RCE did not complete within 24 h on 11 of the 17 datasets. These results indicate that medoid-based cluster representation reduces gene subset size and computational cost within the RCE framework while preserving predictive performance.