mlsanityData to 7 Oct 2026

Classification from Positive, Unlabeled and Biased Negative Data

arXiv 1810.00846 ↗First posted 1 Oct 2018cs.LG stat.MLcited by 34

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Abstract (arXiv)

In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios. However, collecting a non-representative N set that contains only a small portion of all possible N data can often be much easier in practice. This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning. We provide a method based on empirical risk minimization to address this PUbN classification problem. Our approach can be regarded as a novel example-weighting algorithm, with the weight of each example computed through a preliminary step that draws inspiration from PU learning. We also derive an estimation error bound for the proposed method. Experimental results demonstrate the effectiveness of our algorithm in not only PUbN learning scenarios but also ordinary PU learning scenarios on several benchmark datasets.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 5; 2022: 10; 2023: 6; 2024: 4; 2025: 5; 2026: 3.

34 papers.

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  1. 2022"This is my unicorn, Fluffy": Personalizing frozen vision-language representationscited by 95
  2. 2022Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspectivecited by 37
  3. 2019Positive-Unlabeled Reward Learningcited by 36
  4. 2022Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learningcited by 21
  5. 2024Decoupling the Class Label and the Target Concept in Machine Unlearningcited by 12
  6. 2023Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trendscited by 9
  7. 2024Harmfully Manipulated Images Matter in Multimodal Misinformation Detectioncited by 9
  8. 2021PAUSE: Positive and Annealed Unlabeled Sentence Embeddingcited by 5
  9. 2026Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearningcited by 5
  10. 2023Dens-PU: PU Learning with Density-Based Positive Labeled Augmentationcited by 4
  11. 2023Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correctioncited by 4
  12. 2023Positive-Unlabeled Node Classification with Structure-aware Graph Learningcited by 4
22 more
  1. 2022Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learningcited by 3
  2. 2022Positive-Unlabeled Domain Adaptationcited by 3
  3. 2022Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learningcited by 2
  4. 2023Anomaly detection with semi-supervised classification based on risk estimatorscited by 2
  5. 2024Meta-learning for Positive-unlabeled Classificationcited by 2
  6. 2024Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportionscited by 2
  7. 2021A Novel Perspective for Positive-Unlabeled Learning via Noisy Labelscited by 1
  8. 2021A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intentscited by 1
  9. 2021A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervisioncited by 1
  10. 2023Automatic Debiased Learning from Positive, Unlabeled, and Exposure Datacited by 1
  11. 2025Few Dimensions are Enough: Fine-tuning BERT with Selected Dimensions Revealed Its Redundant Naturecited by 1
  12. 2025Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithmscited by 1
  13. 2021A Symmetric Loss Perspective of Reliable Machine Learningcited by 0
  14. 2022Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probabilitycited by 0
  15. 2022A Boosting Algorithm for Positive-Unlabeled Learningcited by 0
  16. 2022Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervisioncited by 0
  17. 2022Leveraging Structure for Improved Classification of Grouped Biased Datacited by 0
  18. 2025Detecting Scarce and Sparse Anomalous: Solving Dual Imbalance in Multi-Instance Learningcited by 0
  19. 2025Angular Regularization for Positive-Unlabeled Learning on the Hyperspherecited by 0
  20. 2025Learning Robust Representations for Malicious Content Detection via Contrastive Sampling and Uncertainty Estimationcited by 0
  21. 2026Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarioscited by 0
  22. 2026PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inferencecited by 0