Binary Classification

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Overview

Binary classification studies how to learn decision rules that separate two classes from labeled data, and how to make their predictions useful when labels are scarce, noisy, or unevenly distributed. Common approaches include linear models and support vector machines, trees and boosting, neural networks, and positive-unlabeled learning, alongside threshold adjustment, reweighting, and abstention. Theoretical work examines generalization, calibration, privacy, and robustness, while empirical studies use settings ranging from tabular and biomedical data to text, images, and security tasks. A central tension is that accuracy or AUC alone may not predict performance at operational thresholds, where minority-class recall, false alarms, calibration, and distribution shift matter.

Latest papers

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67 papers6 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

  1. Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling

    Feb 21, 2025Yufan Li, Pragya SurProbability CalibrationBinary Classification

    Department of Statistics Harvard University Cambrdige, MA 02138

  2. Hierarchical Sparse Bayesian Multitask Learning for Disease Prediction in Pooled Microbiome Studies

    Feb 4, 2025Haonan Zhu, Andre R. Goncalves, Camilo Valdes +10Clinical Outcome PredictionMulti-Task Learning

    Lawrence Livermore National Laboratory · Electrical and Computer Engineering, University of Michigan

  3. Universality of Benign Overfitting in Binary Linear Classification

    Jan 17, 2025Ichiro Hashimoto, Stanislav Volgushev, Piotr ZwiernikNeural Network GeneralizationLinear Classification

    Department of Statistical Sciences, University of Toronto

  4. Optimal Symmetries in Binary Classification

    Aug 16, 2024Vishal S. Ngairangbam, Michael SpannowskyEquivariant Neural NetworksBinary Classification

    Department of Physics, Durham University Durham DH1 3LE, United Kingdom

  5. Pairwise Difference Learning for Classification

    Jun 28, 2024Mohamed Karim Belaid, Maximilian Rabus, Eyke HüllermeierClassificationMeta-Learning

    IDIADA Fahrzeugtechnik GmbH, Munich, Germany · Dr. Ing. h.c. F. Porsche AG Stuttgart, Germany · Ludwig-Maximilians-Universität Munich, Germany

  6. Robust performance metrics for imbalanced classification problems

    Apr 11, 2024Hajo Holzmann, Bernhard KlarClass-Imbalanced LearningBinary Classification

    Fachbereich Mathematik und Informatik, Philipps-Universität Marburg, Hans-Meerweinstr. 6, 35043 Marburg, Germany · Institut für Stochastik, Karlsruher Institut für Technologie (KIT)

  7. Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers

    Jan 8, 2024Qinwu XuBinary Classification

    Leidos Holdings Inc.; The University of Texas at Austin