  We design a new adaptive learning algorithm for misclassification cost
problems that attempt to reduce the cost of misclassified instances derived
from the consequences of various errors. Our algorithm (adaptive cost sensitive
learning - AdaCSL) adaptively adjusts the loss function such that the
classifier bridges the difference between the class distributions between
subgroups of samples in the training and test data sets with similar predicted
probabilities (i.e., local training-test class distribution mismatch). We
provide some theoretical performance guarantees on the proposed algorithm and
present empirical evidence that a deep neural network used with the proposed
AdaCSL algorithm yields better cost results on several binary classification
data sets that have class-imbalanced and class-balanced distributions compared
to other alternative approaches.
