2019/05/23 by Mehmet Yigit Yildirim, Mert Özer, Mert Ozer +4
Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Optimization and Control (math.OC) #Other Statistics (stat.OT) #cs.AI #cs.LG #math.OC #stat.ML #stat.OT
paper · pdf · doi:10.48550/arxiv.1905.09509
arxiv created 2019/05/23 · openalex publication_date 2019/05/23 · arxiv updated 2019/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. In this study, we propose a mixed-integer programming framework for classification with reject option (also known as selective classification), that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. Our results indicate superior performance of our framework both in non-rejected accuracy and rejection quality on several publicly available datasets. Moreover, we extend our framework to cost-sensitive settings and show that our approach outperforms industry standard methods significantly for online fraud management in real-world settings.