Efficient mining of multilevel gene association rules from microarray and gene ontology
Information Systems Frontiers
A new support vector machine for microarray classification and adaptive gene selection
ACC'09 Proceedings of the 2009 conference on American Control Conference
Exploiting the Accumulated Evidence for Gene Selection in Microarray Gene Expression Data
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
International Journal of Approximate Reasoning
An experimental comparison of gene selection by Lasso and Dantzig selector for cancer classification
Computers in Biology and Medicine
Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
CODA: high dimensional copula discriminant analysis
The Journal of Machine Learning Research
Computers in Biology and Medicine
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Motivation: The standard L2-norm support vector machine (SVM) is a widely used tool for microarray classification. Previous studies have demonstrated its superior performance in terms of classification accuracy. However, a major limitation of the SVM is that it cannot automatically select relevant genes for the classification. The L1-norm SVM is a variant of the standard L2-norm SVM, that constrains the L1-norm of the fitted coefficients. Due to the singularity of the L1-norm, the L1-norm SVM has the property of automatically selecting relevant genes. On the other hand, the L1-norm SVM has two drawbacks: (1) the number of selected genes is upper bounded by the size of the training data; (2) when there are several highly correlated genes, the L1-norm SVM tends to pick only a few of them, and remove the rest. Results: We propose a hybrid huberized support vector machine (HHSVM). The HHSVM combines the huberized hinge loss function and the elastic-net penalty. By doing so, the HHSVM performs automatic gene selection in a way similar to the L1-norm SVM. In addition, the HHSVM encourages highly correlated genes to be selected (or removed) together. We also develop an efficient algorithm to compute the entire solution path of the HHSVM. Numerical results indicate that the HHSVM tends to provide better variable selection results than the L1-norm SVM, especially when variables are highly correlated. Availability: R code are available at http://www.stat.lsa.umich.edu/~jizhu/code/hhsvm/ Contact: jizhu@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.