Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation
arxiv(2022)
摘要
Cost-sensitive classification is critical in applications where
misclassification errors widely vary in cost. However, over-parameterization
poses fundamental challenges to the cost-sensitive modeling of deep neural
networks (DNNs). The ability of a DNN to fully interpolate a training dataset
can render a DNN, evaluated purely on the training set, ineffective in
distinguishing a cost-sensitive solution from its overall accuracy maximization
counterpart. This necessitates rethinking cost-sensitive classification in
DNNs. To address this challenge, this paper proposes a cost-sensitive
adversarial data augmentation (CSADA) framework to make over-parameterized
models cost-sensitive. The overarching idea is to generate targeted adversarial
examples that push the decision boundary in cost-aware directions. These
targeted adversarial samples are generated by maximizing the probability of
critical misclassifications and used to train a model with more conservative
decisions on costly pairs. Experiments on well-known datasets and a pharmacy
medication image (PMI) dataset made publicly available show that our method can
effectively minimize the overall cost and reduce critical errors, while
achieving comparable performance in terms of overall accuracy.
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