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Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation

Author

Listed:
  • Qiyuan Chen

    (Industrial & Operation Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Raed Al Kontar

    (Industrial & Operation Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Maher Nouiehed

    (Industrial Engineering, American University of Beirut, Beirut 1107 2020, Lebanon)

  • X. Jessie Yang

    (Industrial & Operation Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Corey Lester

    (College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, overparameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training data set 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 overparameterized 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 data sets and a pharmacy medication image (PMI) data set, made publicly available, show that our method can effectively minimize the overall cost and reduce critical errors while achieving comparable overall accuracy.

Suggested Citation

  • Qiyuan Chen & Raed Al Kontar & Maher Nouiehed & X. Jessie Yang & Corey Lester, 2025. "Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation," INFORMS Joural on Data Science, INFORMS, vol. 4(1), pages 1-19, January.
  • Handle: RePEc:inm:orijds:v:4:y:2025:i:1:p:1-19
    DOI: 10.1287/ijds.2022.0033
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