Partial-Label Learning with a Reject Option Article Swipe

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Tobias A. Fuchs , Florian Kalinke , Klemens Böhm ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2402.00592

In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classifiers in this weakly supervised setting, where state-of-the-art methods already show good predictive performance. However, even the best algorithms give incorrect predictions, which can have severe consequences when they impact actions or decisions. We propose a novel risk-consistent nearest-neighbor-based partial-label learning algorithm with a reject option, that is, the algorithm can reject unsure predictions. Extensive experiments on artificial and real-world datasets show that our method provides the best trade-off between the number and accuracy of non-rejected predictions when compared to our competitors, which use confidence thresholds for rejecting unsure predictions. When evaluated without the reject option, our nearest-neighbor-based approach also achieves competitive prediction performance.

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Computer Science

Artificial Intelligence

Medicine

Internal Medicine

Antagonist

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Partial agonist Computer science Artificial intelligence Medicine Internal medicine Receptor Antagonist

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2402.00592
PDF: https://arxiv.org/pdf/2402.00592
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4391506122

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OpenAlex ID: https://openalex.org/W4391506122

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DOI: https://doi.org/10.48550/arxiv.2402.00592

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Title: Partial-Label Learning with a Reject Option

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-02-01

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Authors: Tobias A. Fuchs, Florian Kalinke, Klemens Böhm

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Landing page: https://arxiv.org/abs/2402.00592

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PDF URL: https://arxiv.org/pdf/2402.00592

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Open access: Yes

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OA URL: https://arxiv.org/pdf/2402.00592

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Concepts: Partial agonist, Computer science, Artificial intelligence, Medicine, Internal medicine, Receptor, Antagonist

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Cited by: 0

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