Research map / Label-efficient and robust learning / Recognition and long-tail
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply ReCPE regrouping to AlphaMax | AlphaMax · Real-world datasets (average) | absolute estimation error: 0.116 -> 0.1 | Helped |
| Apply ReCPE regrouping to DEDPUL | DEDPUL · Real-world datasets (average) | absolute estimation error: 0.094 -> 0.092 | Helped |
| Apply ReCPE regrouping to Elkan-Noto | Elkan-Noto · Real-world datasets (average) | absolute estimation error: 0.311 -> 0.297 | Helped |
| Apply ReCPE regrouping to KM1 | KM1 · Real-world datasets (average) | absolute estimation error: 0.146 -> 0.121 | Helped |
| Apply ReCPE regrouping to KM2 | KM2 · Real-world datasets (average) | absolute estimation error: 0.117 -> 0.111 | Helped |
| Apply ReCPE regrouping to ROC | ROC · Real-world datasets (average) | absolute estimation error: 0.157 -> 0.136 | Helped |
| Apply ReCPE regrouping to Rankpruning | Rankpruning · Real-world datasets (average) | absolute estimation error: 0.106 -> 0.105 | Helped |
| Apply ReCPE regrouping to existing CPE estimators | CPE methods on real-world datasets · Real-world datasets, across datasets and sample sizes | absolute estimation error | Mixed |
| Use a copy fraction of 10% | ReCPE · Experiments across datasets | absolute estimation error | Helped |
Closest papers by meaning
- Learning from Positive and Unlabeled Data with Arbitrary Positive Shift (NeurIPS 2020 · similarity 0.92)
- Balancing Positive and Negative Classification Error Rates in Positive-Unlabeled Learning (NeurIPS 2025 · similarity 0.91)
- Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms (ICLR 2026 · similarity 0.90)
- Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning (NeurIPS 2016 · similarity 0.90)
- Learning from positive and unlabeled examples -Finite size sample bounds (NeurIPS 2025 · similarity 0.90)
- Positive and Unlabeled Learning with Controlled Probability Boundary Fence (ICML 2024 · similarity 0.87)
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