Summary of Meta-learning For Positive-unlabeled Classification, by Atsutoshi Kumagai et al.
Meta-learning for Positive-unlabeled Classification
by Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper proposes a meta-learning approach for positive and unlabeled (PU) classification, which improves the performance of binary classifiers trained on PU data in unseen target tasks. The authors aim to address the limitations of existing PU learning methods that require large amounts of PU data, often unavailable in practice. Instead, they propose an adaptation process using related tasks with positive, negative, and unlabeled data. This approach minimizes the test classification risk by estimating the Bayes optimal classifier, which is formulated as a density-ratio estimation problem. The authors demonstrate the effectiveness of their method on one synthetic and three real-world datasets, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in machine learning called positive and unlabeled (PU) classification. This means trying to teach computers to tell good data from bad data when you only have examples of both types. The current ways of doing this need lots of data, but sometimes we don’t have that much. So the authors came up with a new way to do it by using related tasks that have more information. They show that their method works better than others on some real-world problems. |
Keywords
» Artificial intelligence » Classification » Machine learning » Meta learning
