Abstract
The large number of genes and the relatively small number of samples are typical characteristics for microarray data. These characteristics pose challenges for both sample classification and relevant gene selection. The support vector machine (SVM) is a widely used classification technique, and previous studies have demonstrated its superior classification performance in microarray analysis. However, a major limitation is that the SVM can not perform automatic gene selection. To overcome this limitation, we propose the hybrid huberized support vector machine (HHSVM). The HHSVM uses the huberized hinge loss function and the elastic-net penalty. It has two major benefits: 1. automatic gene selection; 2. the grouping effect, where highly correlated genes tend to be selected/removed together. We also develop an efficient algorithm that computes the entire regularized solution path for HHSVM. We have applied our method to real microarray data and achieved promising results.
Original language | English (US) |
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Pages | 983-990 |
Number of pages | 8 |
DOIs | |
State | Published - 2007 |
Event | 24th International Conference on Machine Learning, ICML 2007 - Corvalis, OR, United States Duration: Jun 20 2007 → Jun 24 2007 |
Other
Other | 24th International Conference on Machine Learning, ICML 2007 |
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Country/Territory | United States |
City | Corvalis, OR |
Period | 6/20/07 → 6/24/07 |