A Hybrid Feature Selection Approach by Correlation-based Filters and SVM-RFE

Selecting a feature subset with strong discriminative power is a critical process for high dimensional data analysis, which has attracted much attention in many application domains, such as text categorization and genome projects. Since traditional feature selection methods provide limited contributions to classification, many researchers resort to hybrid or elaborate approaches to choose interesting features. In this paper, we propose a novel hybrid approach by correlation-based filters and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method for robust feature selection, which aims to yield robust results by aggregating multiple feature subsets (groups). Specifically, in the first stage, we incorporate correlation-based filters to identify Predominant Features and Complementary Features, and generate multiple groups for robustness; in the second stage, we aggregate multiple groups with SVM-RFE into a compact feature subset for high classification accuracy. Extensive experimental studies on both UCI data sets and microarray data sets have confirmed the effectiveness of our proposed approach.