Canadian Journal of Statistics · 2006 · 194 citations · 14 references
BusinessDecision SciencesDecision AnalysisDecision TheoryProbability TheoryClassificationConditional ProbabilityDecision Making Under RiskDecision Under RiskDecision Under UncertaintyProbabilistic Machine LearningBinary ClassificationEmpirical Bayes Risk EstimationEmpirical Risk ManagementRejection RulesStatistical Decision Theory
Abstract The authors study binary classification that allows for a reject option in which case no decision is made. This reject option is to be used for those observations for which the conditional class probabilities are close and as such are hard to classify. The authors generalize existing theory for both plug‐in rules and empirical risk minimizers to this setting.
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Optimal aggregation of classifiers in statistical learning
A. B. Tsybakov · The Annals of Statistics · 2004 · 601 citations · Full text
The Annals of Statistics · 2005 · 346 citations · Full text