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- W2134837462 abstract "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." @default.
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- W2134837462 date "2005-12-01" @default.
- W2134837462 modified "2023-10-16" @default.
- W2134837462 title "APPLICATIONS OF SUPPORT VECTOR MACHINES TO CANCER CLASSIFICATION WITH MICROARRAY DATA" @default.
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- W2134837462 doi "https://doi.org/10.1142/s0129065705000396" @default.
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