WITHDRAWN: Margin Weighted Robust Discriminant Score for Feature Selection in Imbalanced Gene Expression Class

Music City Center 

Feature selection for high-dimensional gene expression classification faces significant challenges. Conventional procedures such as Wilcoxon Rank-Sum Test, Proportional Overlap Score, Weighted Signal-to-Noise Ratio, Fisher Score, ensemble Minimum Redundancy Maximum Relevance, etc. struggle with the issues of redundancy and class imbalance, often inadequately representing the minority class. To solve these issues, this work proposes the Margin Weighted Robust Discriminant Score (MW-RDS) as a novel feature selection method for high-dimensional imbalanced data. MW-RDS uses a Minority Amplification Factor to amplify observations in the minority class, coupled with robust discriminant score (RDS) based class-specific stability weights. Margin weights derived using support vectors improve the discriminative capability of genes. The gene set is further refined using l1-regularization, reducing redundancy. The procedure is assessed on 9 high dimensional gene expression and simulated datasets using three classifiers, with superior performance across several metrics. Additional visualization via Boxplots and stability plots further validates the efficacy of the proposed method.

Classification

High dimensional gene expression datasets

Feature selection 

Section on Statistical Computing