Gene Selection Using 1-Norm Regularization For Multi-Class Microarray Data
Computing Sciences and Computer Engineering
Explosive compounds such as TNT and RDX have various toxicological effects on the natural environment. The goal of the earthworm microarray experiment is to unearth the biomarker for toxicity evaluation. We propose a novel recursive gene selection method which can handle the multi-class setting effectively and efficiently. The selection is performed iteratively. In each iteration, a linear multi-class classifier is trained using 1-norm regularization, which leads to sparse weight vectors, i.e., many feature weights are exactly zero. Those zero-weight features are eliminated in the next iteration. The empirical results demonstrate that the selected features (genes) have very competitive discriminative power. In addition, the selection process has fast rate of convergence. ©2010 IEEE.
Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
(2010). Gene Selection Using 1-Norm Regularization For Multi-Class Microarray Data. Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, 520-524.
Available at: https://aquila.usm.edu/fac_pubs/17940