Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

• Published in: Genomics, proteomics & bioinformatics Vol. 15; no. 6; pp. 389 - 395
• Main Authors: Gao, Lingyun, Ye, Mingquan, Lu, Xiaojie, Huang, Daobin
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01.12.2017; School of Medical Information, Wannan Medical College, Wuhu 241002, China; Elsevier; Oxford University Press
• Subjects: Cancer classification; Computational Biology - methods; Databases, Genetic; Gene Expression Regulation, Neoplastic; Gene selection; Genes, Neoplasm; Humans; Information gain; Method; Neoplasms - classification; Neoplasms - genetics; Small sample size with high dimension; Support Vector Machine; Gene selection; Support vector machine; Information gain; Cancer classification; Small sample size with high dimension
• ISSN: 1672-0229; 2210-3244
• DOI: 10.1016/j.gpb.2017.08.002

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine （IG-SVM） in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYLg, and GUCA2B.