Sparse support vector machines with Lp penalty for biomarker identification

Zhenqiu Liu, Shili Lin, Ming Tan

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

The development of high-throughput technology has generated a massive amount of high-dimensional data, and many of them are of discrete type. Robust and efficient learning algorithms such as LASSO [1] are required for feature selection and overfitting control. However, most feature selection algorithms are only applicable to the continuous data type. In this paper, we propose a novel method for sparse support vector machines (SVMs) with Lp (p 1) regularization. Efficient algorithms (LpSVM) are developed for learning the classifier that is applicable to high-dimensional data sets with both discrete and continuous data types. The regularization parameters are estimated through maximizing the area under the ROC curve (AUC) of the cross-validation data. Experimental results on protein sequence and SNP data attest to the accuracy, sparsity, and efficiency of the proposed algorithm. Biomarkers identified with our methods are compared with those from other methods in the literature. The software package in Matlab is available upon request.

Original languageEnglish (US)
Article number4459304
Pages (from-to)100-107
Number of pages8
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume7
Issue number1
DOIs
StatePublished - Jan 1 2010

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics

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