RCE-IFE: recursive cluster elimination with intra-cluster feature elimination.

ABSTRACT: The computational and interpretational difficulties caused by the ever-increasing dimensionality of biological data generated by new technologies pose a significant challenge. Feature selection (FS) methods aim to reduce the dimension, and feature grouping has emerged as a foundation for FS techniques that seek to detect strong correlations among features and identify irrelevant features. In this work, we propose the Recursive Cluster Elimination with Intra-Cluster Feature Elimination (RCE-IFE) method that utilizes feature grouping and iterates grouping and elimination steps in a supervised context. We assess dimensionality reduction and discriminatory capabilities of RCE-IFE on various high-dimensional datasets from different biological domains. For a set of gene expression, microRNA (miR

SUBMITTER: Kuzudisli C 

PROVIDER: S-EPMC11888879 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature