Research map / Label-efficient and robust learning / Recognition and long-tail
Class Prior Estimation with Biased Positives and Unlabeled Examples
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add cluster-based bias correction (Algorithm 1) to AlphaMax class prior estimation | AlphaMax · 12 UCI datasets with biased positives | mean absolute error: 0.2277 -> 0.1579 | Helped |
| use the true generating clustering instead of estimated k-means clustering (ablation) | Corrected AlphaMax · 12 UCI datasets with biased positives | mean absolute error: 0.1579 -> 0.0900 | Helped |
| add cluster-based bias correction when positives are actually unbiased | AlphaMax · 12 UCI datasets without bias | mean absolute error: 0.0871 -> 0.1176 | Hurt |
| add cluster-based bias correction on top of Elkan-Noto base estimator | Elkan-Noto · 12 UCI datasets with biased positives | absolute error | Mixed |
Closest papers by meaning
- Recovering True Classifier Performance in Positive-Unlabeled Learning (AAAI 2017 · similarity 0.93)
- Estimating the class prior and posterior from noisy positives and unlabeled data (NeurIPS 2016 · similarity 0.91)
- Learning from Positive and Unlabeled Data without Explicit Estimation of Class Prior (AAAI 2020 · similarity 0.91)
- Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting (AAAI 2020 · similarity 0.91)
- Learning from positive and unlabeled examples -Finite size sample bounds (NeurIPS 2025 · similarity 0.88)
- Can Class-Priors Help Single-Positive Multi-Label Learning? (NeurIPS 2025 · similarity 0.87)
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