ProPML: Probability Partial Multi-label Learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866929273591300096 |
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| author | Struski, Łukasz Pardyl, Adam Tabor, Jacek Zieliński, Bartosz |
| author_facet | Struski, Łukasz Pardyl, Adam Tabor, Jacek Zieliński, Bartosz |
| contents | Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true. In this paper, we introduce \our{}, a novel probabilistic approach to this problem that extends the binary cross entropy to the PML setup. In contrast to existing methods, it does not require suboptimal disambiguation and, as such, can be applied to any deep architecture. Furthermore, experiments conducted on artificial and real-world datasets indicate that \our{} outperforms existing approaches, especially for high noise in a candidate set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07603 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ProPML: Probability Partial Multi-label Learning Struski, Łukasz Pardyl, Adam Tabor, Jacek Zieliński, Bartosz Machine Learning Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true. In this paper, we introduce \our{}, a novel probabilistic approach to this problem that extends the binary cross entropy to the PML setup. In contrast to existing methods, it does not require suboptimal disambiguation and, as such, can be applied to any deep architecture. Furthermore, experiments conducted on artificial and real-world datasets indicate that \our{} outperforms existing approaches, especially for high noise in a candidate set. |
| title | ProPML: Probability Partial Multi-label Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.07603 |