INFORMS journal on data science最新文献

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Sequential Change Point Detection in Dynamic Non-Stationary Manufacturing Processes. 动态非平稳制造过程中的顺序变化点检测。

INFORMS journal on data science Pub Date : 2026-06-12 DOI: 10.1287/ijds.2025.0068

Yuhan Tian, Abolfazl Safikhani, Kamran Paynabar

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引用次数: 0

Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation. 基于对抗数据增强的深度学习中代价敏感分类的再思考。

INFORMS journal on data science Pub Date : 2025-01-01 Epub Date: 2024-02-28 DOI: 10.1287/ijds.2022.0033

Qiyuan Chen, Raed Al Kontar, Maher Nouiehed, X Jessie Yang, Corey Lester

{"title":"Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation.","authors":"Qiyuan Chen, Raed Al Kontar, Maher Nouiehed, X Jessie Yang, Corey Lester","doi":"10.1287/ijds.2022.0033","DOIUrl":"10.1287/ijds.2022.0033","url":null,"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.","PeriodicalId":524165,"journal":{"name":"INFORMS journal on data science","volume":"4 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13480451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148805572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0