Summary of Soft Label Pu Learning, by Puning Zhao et al.
Soft Label PU Learning
by Puning Zhao, Jintao Deng, Xu Cheng
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed soft label PU learning method assigns probabilistic labels to unlabeled data, taking into account their likelihood of being positive samples. This approach addresses the limitation of existing methods that treat all unlabeled samples equally. The paper designs PU counterparts of TPR, FPR, and AUC metrics to evaluate the performance of soft label PU learning methods within validation data. These new metrics are shown to be effective substitutes for the real metrics. A method is proposed that optimizes these metrics, demonstrated on public datasets and real-world datasets from Tencent games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists found a way to make computer learning better by giving some unlabeled information a “soft label” that shows how likely it is to be positive (meaning it matches the labeled data). This helps machines learn more accurately. The team also created new metrics to measure how well their method works and showed that it does well on real-world datasets from games. |
Keywords
» Artificial intelligence » Auc » Likelihood
