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Minimax Regret Classifier for Imprecise Class Distributions
Journal of machine learning research
The design of a minimum risk classifier based on data usually stems from the stationarity assumption that the conditions during training and test are the same: the misclassification costs assumed during training must be in agreement with real costs, and the same statistical process must have generated both training and test data. Unfortunately, in real world applications, these assumptions may not hold. This paper deals with the problem of training a classifier when prior probabilities cannotdblp:journals/jmlr/Alaiz-RodriguezGC07 fatcat:e6hjpani75h5llbht7zv3tlply