Hybrid huberized support vector machines for microarray classification

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

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Li Wang, Ji Zhu, Hui Zou
• Keywords: ICML 2007, Machine Learning, Relevant Gene Selection, Superior Classification Performance, Support Vector Machine