Cost-Sensitive Learning

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Overview

Cost-sensitive learning studies how models should change their predictions when some mistakes or actions are more costly than others. Common approaches reweight examples or losses, adapt penalties to instance difficulty or error severity, and optimize decisions such as test acquisition, abstention, or expert referral. These methods are used in settings including imbalanced classification, medical diagnosis, fraud detection, forecasting, and sequential decision-making, where errors and resources have unequal consequences. A central challenge is specifying costs that reflect real priorities: emphasizing costly cases can reduce rare severe errors, but may trade off performance elsewhere, and lower error costs do not always mean better ranking or calibration.

Latest papers

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42 papers10 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

  1. Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit

    Apr 5, 2026Wenhao Chi, Ş. İlker BirbilCost-Sensitive LearningInterpretable ML

    Yau Mathematical Sciences Center, Tsinghua University · Amsterdam Business School, University of Amsterdam

  2. Cost-Sensitive Neighborhood Aggregation for Heterophilous Graphs: When Does Per-Edge Routing Help?

    Mar 25, 2026Eyal WeissGraph Neural NetworksGraph Heterophily

    Computer Science Faculty, Technion — Israel Institute of Technology

  3. Consistent Learning-to-Defer with Expert-Conditional Advice

    Mar 15, 2026Yannis Montreuil, Leïna Montreuil, Axel Carlier +2Learning to DeferCost-Sensitive Learning

    School of Computing National University of Singapore Singapore, 117418 · Département de Mathématiques Sorbonne University Paris, 75005, France · Fédération ENAC ISAE-SUPAERO ONERA Université de Toulouse Toulouse, 31555, France +1

  4. Every Error has Its Magnitude: Asymmetric Mistake Severity Training for Multiclass Multiple Instance Learning

    Mar 14, 2026Sungrae Hong, Jiwon Jeong, Jisu Shin +4Multiple Instance LearningCost-Sensitive Learning

    Korea Advanced Institute of Science and Technology, Daejeon, South Korea · Seegene Medical Foundation, Seoul, South Korea

  5. FraudFox: Adaptable Fraud Detection in the Real World

    Mar 13, 2026Matthew Butler, Yi Fan, Christos FaloutsosFinancial Fraud DetectionEnsemble Learning

    Amazon.com, Inc., 410 Terry Ave N, Seattle WA 98109, U.S.A. · Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, U.S.A.

  6. Relaxed Efficient Acquisition of Context and Temporal Features

    Mar 11, 2026Yunni Qu, Dzung Dinh, Grant King +5Active Feature AcquisitionFeature Selection

    The University of North Carolina at Chapel Hill · University of North Carolina at Chapel Hill, Chapel Hill, NC, USA · University of Michigan +3

  7. Algorithmic Compliance and Regulatory Loss in Digital Assets

    Mar 4, 2026Khem Raj Bhatt, Krishna SharmaAnti-Money LaunderingAnti-Money Laundering Detection

  8. Prediction of Major Solar Flares Using Interpretable Class-dependent Reward Framework with Active Region Magnetograms and Domain Knowledge

    Feb 18, 2026Zixian Wu, Xuebao Li, Yanfang Zheng +14Solar PhysicsCost-Sensitive Learning

    School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, People′s Republic of China · State Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing 100190, People′s Republic of China · School of software, Southeast University, Nanjing, People′s Republic of China +4

  9. Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models

    Jan 30, 2026Yannis Montreuil, Letian Yu, Axel Carlier +2Cost-Sensitive LearningAdaptive Model Routing

    School of Computing National University of Singapore · Université de Toulouse Fédération ENAC ISAE-SUPAERO ONERA · Institute for Infocomm Research A*STAR, Singapore

  10. Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions

    Sep 17, 2025Michel A. YoussefCost-Sensitive LearningProbability Calibration

    Independent Researcher · Independent Researcher, Lebanon

  11. Model-agnostic Mitigation Strategies of Data Imbalance for Regression

    Jun 2, 2025Jelke Wibbeke, Sebastian Rohjans, Andreas RauhCost-Sensitive Learning

    Department of Computing Science Carl von Ossietzky Universität Oldenburg · Energy Division, OFFIS - Institute of Information Technology, Oldenburg, Germany · Department for Civil Engineering, Geoinformation and Health Technology Jade University of Applied Science

  12. iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework

    Sep 19, 2024Asif Newaz, Asif Ur Rahman Adib, Taskeed JabidClass-Imbalanced LearningCost-Sensitive Learning

    Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh · Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka, Bangladesh · Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh