An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data

• DOI: 10.1093/bioinformatics/bts602
• Publication Date: 2012-10-12T00:24:35Z
• Authors: Yongjun Piao; Minghao Piao; Kiejung Park; Keun Ho Ryu
• Keywords: Gene selection; Discriminative model; Relevance; Ensemble Learning; Data set

Abstract Motivation: Gene selection for cancer classification is one of the most important topics in biomedical field. However, microarray data pose a severe challenge computational techniques. We need dimension reduction techniques that identify small set genes to achieve better learning performance. From perspective machine learning, can be considered feature problem aims find subset features has discriminative information target. Results: In this article, we proposed an Ensemble Correlation-Based Selection algorithm based on symmetrical uncertainty and Support Vector Machine. our method, was used analyze relevance genes, different starting points relevant were generate gene subsets Machine as evaluation criterion wrapper. The efficiency effectiveness method demonstrated through comparisons with other techniques, results show outperformed methods published literature. Availability: By request from author. Contact: [e-mail]; [e-mail]