Optimal convex error estimators for classification

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Optimal convex error estimators for classification
A cross-validation error estimator is obtained by repeatedly leaving out some data points, deriving classifiers on the remaining points, computing errors for these classifiers on the left-out points, and then averaging these errors. The 0.632 bootstrap estimator is obtained by averaging the errors of classifiers designed from points drawn with replacement and then taking a convex combination of this "zero bootstrap" error with the resubstitution error for the designed classifier. This gives a convex combination of the low-biased resubstitution and the high-biased zero bootstrap. Another convex error estimator suggested in the literature is the unweighted average of resubstitution and cross-validation. This paper treats the following question: Given a feature-label distribution and classification rule, what is the optimal convex combination of two error estimators, i.e. what are the optimal weights for the convex combination. This problem is considered by finding the weights ...
Chao Sima, Edward R. Dougherty
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PR
Authors Chao Sima, Edward R. Dougherty
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