Applications of support vector machines to cancer classification with microarray data

作者：Chu F^*; Wang LP

来源：International Journal of Neural Systems, 2005, 15(6): 475-484.

DOI：10.1142/S0129065705000396

• cancer classification
• gene expression data
• microarray
• support vector machine

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.

• 出版日期2005-12
• 单位南阳理工学院