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 major 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 the existence of irrelevant features. In this work, we develop Recursive Cluster Elimination with Intra-Cluster Feature Elimination (RCE-IFE), a method that iterates clustering and elimination steps in a supervised context. Recursively, feature clusters are formed, then scored, and less contributing clusters are eliminated. Next, low-scoring features in retained clusters are eliminated. Intra-cluster feature elimination aims to reduce noisy features while keeping a minimum number of predictive features. The performance of RCE-IFE is evaluated and compared to other FS techniques in several datasets. The results show that the proposed strategy effectively reduces the size of the feature set and also improves the model performance.