Estimation of the conditional risk in classification: The swapping method

Jean-Jacques Daudin and Tristan Mary-Huard

Computational Statistics & Data Analysis, 2008, vol. 52, issue 6, 3220-3232

Abstract: The bias of the empirical error rate in supervised classification is studied. It is shown that this bias can be understood as a covariance between the classification rule and the labeling of the training data. From this result, a new penalized criterion is proposed to perform model selection in classification. Applications of the resulting algorithm to simulated and real data are presented.

Date: 2008
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