mlsanityData to 7 Oct 2026

Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation

arXiv 2107.05045 ↗First posted 11 Jul 2021cs.LGcited by 7

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Abstract (arXiv)

Learning from positive and unlabeled (PU) data is an important problem in various applications. Most of the recent approaches for PU classification assume that the class-prior (the ratio of positive samples) in the training unlabeled dataset is identical to that of the test data, which does not hold in many practical cases. In addition, we usually do not know the class-priors of the training and test data, thus we have no clue on how to train a classifier without them. To address these problems, we propose a novel PU classification method based on density ratio estimation. A notable advantage of our proposed method is that it does not require the class-priors in the training phase; class-prior shift is incorporated only in the test phase. We theoretically justify our proposed method and experimentally demonstrate its effectiveness.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 11 Jul 2021.

Builds on

17 of 58 references.

References · 17

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5 more
  1. 2016Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learningcited by 41
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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 3.

7 papers.

  1. 2022A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labelingcited by 9
  2. 2024Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Datacited by 2
  3. 2024Meta-learning for Positive-unlabeled Classificationcited by 2
  4. 2025Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generationcited by 2
  5. 2022Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Datacited by 0
  6. 2025Prior shift estimation for positive unlabeled data through the lens of kernel embeddingcited by 0
  7. 2025A generalized approach to label shift: the Conditional Probability Shift Modelcited by 0