Lasso based gene selection for linear classifiers
Abstract
Selecting a subset of genes with strong discriminative power is a very important step in classification problems based on gene expression data. Lasso is known to have automatic variable selection ability in linear regression analysis. This paper uses Lasso to select most informative genes to represent the class label as a linear function of the gene expression data. The selected genes are further used to fit linear classifiers for tumor classification. The proposed approach (gene selection and linear classification) was applied to 5 publicly available cancer datasets. Compared to other methods in literature, the proposed method achieves similar or higher classification accuracy with fewer genes.
Department(s)
Mathematics
Document Type
Conference Proceeding
DOI
https://doi.org/10.1109/BIBMW.2009.5332127
Keywords
Cross validation, Lasso, Leave-one-out, Linear classifier, Variable selection
Publication Date
12-1-2009
Recommended Citation
Zheng, Songfeng, and Weixiang Liu. "Lasso based gene selection for linear classifiers." In 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop, pp. 203-208. IEEE, 2009.
Journal Title
Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
