A multiclass, k-NN approach to Bayes risk estimation
Pattern Recognition Letters
Exploiting quadratic mutual information for discriminant analysis
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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The problem of estimating the error probability of a given classification system is considered. Statistical properties of the empirical error count (C) and the average conditional error (R) estimators are studied. It is shown that in the large sample case the R estimator is unbiased and its variance is less than that of the C estimator. In contrast to conventional methods of Bayes error estimation the unbiasedness of the R estimator for a given classifier can be obtained only at the price of an additional set of classified samples. On small test sets the R estimator may be subject to a pessimistic bias caused by the averaging phenomenon characterizing the functioning of conditional error estimators.