Applications of support vector machines to cancer classification with microarray data.
Microarray gene expression data usually have a large number of dimensions, e.g., over ten thousand genes, and a small number of samples, e.g., a few tens of patients. In this paper, we use the support vector machine (SVM) for cancer classification with microarray data. Dimensionality reduction methods, such as principal components analysis (PCA), class-separability measure, Fisher ratio, and t-test, are used for gene selection. A voting scheme is then employed to do multi-group classification by k(k - 1) binary SVMs. We are able to obtain the same classification accuracy but with much fewer features compared to other published results.
Chu
Feng
F
School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, Singapore 639798.
Wang
Lipo
L
D000465:Algorithms; D020869:Gene Expression Profiling / Q000295:instrumentation / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods; D010363:Pattern Recognition, Automated; D012680:Sensitivity and Specificity; D012984:Software