Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms

• URL: http://arxiv.org/abs/2509.24228v1
• Date: Mon, 29 Sep 2025 03:13:00 GMT
• Title: Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
• Authors: Wei Wang, Dong-Dong Wu, Ming Li, Jingxiong Zhang, Gang Niu, Masashi Sugiyama,
• Abstract summary: We propose the first PU learning benchmark to systematically compare PU learning algorithms.<n>We identify subtle yet critical factors that affect the realistic and fair evaluation of PU learning algorithms.
• Score: 54.58593451541316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Positive-unlabeled (PU) learning is a weakly supervised binary classification problem, in which the goal is to learn a binary classifier from only positive and unlabeled data, without access to negative data. In recent years, many PU learning algorithms have been developed to improve model performance. However, experimental settings are highly inconsistent, making it difficult to identify which algorithm performs better. In this paper, we propose the first PU learning benchmark to systematically compare PU learning algorithms. During our implementation, we identify subtle yet critical factors that affect the realistic and fair evaluation of PU learning algorithms. On the one hand, many PU learning algorithms rely on a validation set that includes negative data for model selection. This is unrealistic in traditional PU learning settings, where no negative data are available. To handle this problem, we systematically investigate model selection criteria for PU learning. On the other hand, the problem settings and solutions of PU learning have different families, i.e., the one-sample and two-sample settings. However, existing evaluation protocols are heavily biased towards the one-sample setting and neglect the significant difference between them. We identify the internal label shift problem of unlabeled training data for the one-sample setting and propose a simple yet effective calibration approach to ensure fair comparisons within and across families. We hope our framework will provide an accessible, realistic, and fair environment for evaluating PU learning algorithms in the future.

Related papers

• Direct Preference Optimization with Rating Information: Practical Algorithms and Provable Gains [67.71020482405343]
  We study how to design algorithms that can leverage additional information in the form of rating gap.<n>We present new algorithms that can achieve faster statistical rates than DPO in presence of accurate rating gap information.
  arXiv  Detail & Related papers  (2026-01-31T08:38:21Z)
• Realistic Evaluation of Deep Partial-Label Learning Algorithms [94.79036193414058]
  Partial-label learning (PLL) is a weakly supervised learning problem in which each example is associated with multiple candidate labels and only one is the true label.<n>In recent years, many deep algorithms have been developed to improve model performance.<n>Some early developed algorithms are often underestimated and can outperform many later algorithms with complicated designs.
  arXiv  Detail & Related papers  (2025-02-14T14:22:16Z)
• Probably Approximately Precision and Recall Learning [62.912015491907994]
  Precision and Recall are foundational metrics in machine learning. One-sided feedback--where only positive examples are observed during training--is inherent in many practical problems. We introduce a PAC learning framework where each hypothesis is represented by a graph, with edges indicating positive interactions.
  arXiv  Detail & Related papers  (2024-11-20T04:21:07Z)
• Meta-learning for Positive-unlabeled Classification [40.11462237689747]
  The proposed method minimizes the test classification risk after the model is adapted to PU data. The method embeds each instance into a task-specific space using neural networks. We empirically show that the proposed method outperforms existing methods with one synthetic and three real-world datasets.
  arXiv  Detail & Related papers  (2024-06-06T01:50:01Z)
• Understanding Contrastive Representation Learning from Positive Unlabeled (PU) Data [28.74519165747641]
  We study the problem of Positive Unlabeled (PU) learning, where only a small set of labeled positives and a large unlabeled pool are available.<n>We introduce Positive Unlabeled Contrastive Learning (puCL), an unbiased and variance reducing contrastive objective.<n>When the class prior is known, we propose Positive Unlabeled InfoNCE (puNCE), a prior-aware extension that re-weights unlabeled samples as soft positive negative mixtures.
  arXiv  Detail & Related papers  (2024-02-08T20:20:54Z)
• Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation when the SCAR assumption does not hold [2.76815720120527]
  Positive and Unlabeled (PU) learning is a type of semi-supervised binary classification. PU learning has broad applications in settings where confirmed negatives are unavailable or difficult to obtain. We propose two PU learning algorithms to estimate $alpha$, calculate probabilities for PU instances, and improve classification metrics.
  arXiv  Detail & Related papers  (2023-03-14T23:16:22Z)
• Learning from Positive and Unlabeled Data with Augmented Classes [17.97372291914351]
  We propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) We derive the estimation error bound for the proposed estimator, which provides a theoretical guarantee for its convergence to the optimal solution.
  arXiv  Detail & Related papers  (2022-07-27T03:40:50Z)
• Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective [67.45111837188685]
  Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data. We experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning.
  arXiv  Detail & Related papers  (2022-06-16T11:44:11Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms [0.0]
  This study is the most comprehensive of its kind. It considers fairness, predictive-performance, calibration quality, and speed of 28 different modelling pipelines. We also found that fairness-aware algorithms can induce fairness without material drops in predictive power.
  arXiv  Detail & Related papers  (2020-10-08T13:58:09Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Collective Loss Function for Positive and Unlabeled Learning [19.058269616452545]
  We propose a Collectively loss function to learn from only Positive and Unlabeled data. Results show that cPU consistently outperforms the current state-of-the-art PU learning methods.
  arXiv  Detail & Related papers  (2020-05-06T03:30:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.