# A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models

arXiv: https://arxiv.org/abs/1402.3144 · HTML: https://mlsanity.com/paper/1402.3144/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2014.

## Built on by (most cited first, 7 of 7)

- [Fairness-aware Model-agnostic Positive and Unlabeled Learning](https://mlsanity.com/paper/2206.09346/) (2022)
- [An Effective Approach for Multi-label Classification with Missing Labels](https://mlsanity.com/paper/2210.13651/) (2022)
- [Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement](https://mlsanity.com/paper/2206.02423/) (2022)
- [PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision](https://mlsanity.com/paper/2407.06698/) (2024)
- [NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification](https://mlsanity.com/paper/2108.06158/) (2021)
- [An Effective Flow-based Method for Positive-Unlabeled Learning: 2-HNC](https://mlsanity.com/paper/2505.08212/) (2025)
- [Focused PU learning from imbalanced data](https://mlsanity.com/paper/2605.14467/) (2026)

## Abstract (arXiv)

We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap resamples of the training data for increased robustness against label noise. The approach can be considered in a bagging framework which provides an intuitive explanation for its mechanics in a semi-supervised setting. We compared our method to state-of-the-art approaches in simulations using multiple public benchmark data sets. The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives. Our approach shows a marginal improvement over existing methods in the second setting and a significant improvement in the third.