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We design a new adaptive learning algorithm for misclassification costproblems that attempt to reduce the cost of misclassified instances derivedfrom the consequences of various errors. Our algorithm (adaptive cost sensitivelearning - AdaCSL) adaptively adjusts the loss function such that theclassifier bridges the difference between the class distributions betweensubgroups of samples in the training and test data sets with similar predictedprobabilities (i.e., local training-test class distribution mismatch). Weprovide some theoretical performance guarantees on the proposed algorithm andpresent empirical evidence that a deep neural network used with the proposedAdaCSL algorithm yields better cost results on several binary classificationdata sets that have class-imbalanced and class-balanced distributions comparedto other alternative approaches.

Adaptive Cost-Sensitive Learning in Neural Networks for Misclassification Cost Problems