New Framework Improves Biased Positive-Unlabeled Learning with Causal Inference.
Summary
This paper introduces PUe, a framework enhancing Positive-Unlabeled (PU) learning by addressing selection bias in real-world datasets. It uses normalized propensity scores and inverse probability weighting to improve binary classification accuracy with limited positive labels.
Why it matters
Professionals dealing with imbalanced datasets or limited labeled data can leverage this framework to build more accurate and reliable classification models, especially in domains where labeling is costly or difficult.
How to implement this in your domain
- 1Evaluate existing PU learning pipelines for potential selection bias in data labeling.
- 2Integrate propensity score estimation techniques to quantify and correct for bias in unlabeled data.
- 3Apply normalized inverse probability weighting within your PU learning objective function.
- 4Test the PUe framework's effectiveness on your specific datasets, comparing it against current baselines.
- 5Consider using deep learning for propensity score estimation to handle complex data distributions.
Who benefits
Key takeaways
- Real-world PU learning often suffers from selection bias due to non-random label distributions.
- The PUe framework uses causal inference and normalized weighting to correct for this bias.
- It improves classification accuracy, especially with uneven label distributions and limited positive examples.
- The framework offers theoretical grounding and practical integration with existing PU methods.
Original post by Xutao Wang, Hanting Chen, Tianyu Guo, Yunhe Wang
"arXiv:2607.13428v1 Announce Type: new Abstract: Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones. Existing cost-sensitive-based methods often rely on strong assumptions that exa…"
View on XOriginally posted by Xutao Wang, Hanting Chen, Tianyu Guo, Yunhe Wang on X · view source
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