Summary of Partial-label Learning with a Reject Option, by Tobias Fuchs et al.
Partial-Label Learning with a Reject Option
by Tobias Fuchs, Florian Kalinke, Klemens Böhm
First submitted to arxiv on: 1 Feb 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 This paper proposes a novel algorithm for partial-label learning, a setting where data is ambiguously labeled by different annotators. The goal is to train classifiers that can predict accurately despite this uncertainty. Existing methods already perform well, but there’s still room for improvement. The proposed approach uses a nearest-neighbor-based method with a reject option, allowing the algorithm to avoid making predictions when it’s unsure. This leads to better trade-offs between prediction accuracy and number of non-rejected predictions compared to existing methods. The results are demonstrated on both artificial and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can learn from data that has been labeled incorrectly or partially correctly. This happens often in real life, like when people disagree on what something means. To fix this problem, the researchers developed a new way to teach computers to make predictions even with uncertain data. They call it partial-label learning. Their method uses a special kind of learning called nearest-neighbor-based and adds an option for the computer to say “I’m not sure” when it’s unsure about what to predict. This helps reduce mistakes. The researchers tested their approach on both fake and real-world data and found that it works well. |
Keywords
* Artificial intelligence * Nearest neighbor
