Focused PU learning from imbalanced data
We propose a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets. Many real-world problems, such as disease gene identification, targeted marketing, fraud detection, and recommender systems, are hard to address with machine learning methods, due to limited labeled data. Often, training data comprises positive and unlabeled instances, the latter typically being dominated by negative, but including also several positive instances. While PU learning is well-studied, few methods address imbalanced settings or hard-to-detect positive examples that resemble negative ones. Our approach uses a focused empirical risk estimator, incorporating both positive...
| Authors | Elias Zavitsanos, Georgios Paliouras |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 21 September 2026 |
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