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Correlation is a very widely used filter criterion for gene selection in cancer classification. However, it uses all the training samples in ranking, which may not be equally important for the classification. Using support vectors, we demonstrate that classical correlation coefficient based gene selection is biased because of the sample points away from classification margin. To remove such bias,...
In classification problems, the class imbalance problem will cause a bias on the training of classifiers and will result in the lower sensitivity of detecting the minority class examples. The Mahalanobis-Taguchi System (MTS) is a diagnostic and forecasting technique for multivariate data. MTS establishes a classifier by constructing a continuous measurement scale rather than directly learning from...
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