milearn: A Python Package for Multi-Instance Machine Learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912740458627072 |
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| author | Zankov, Dmitry Polishchuk, Pavlo Sobieraj, Michal Barbatti, Mario |
| author_facet | Zankov, Dmitry Polishchuk, Pavlo Sobieraj, Michal Barbatti, Mario |
| contents | We introduce milearn, a Python package for multi-instance learning (MIL) that follows the familiar scikit-learn fit/predict interface while providing a unified framework for both classical and neural-network-based MIL algorithms for regression and classification. The package also includes built-in hyperparameter optimization designed specifically for small MIL datasets, enabling robust model selection in data-scarce scenarios. We demonstrate the versatility of milearn across a broad range of synthetic MIL benchmark datasets, including digit classification and regression, molecular property prediction, and protein-protein interaction (PPI) prediction. Special emphasis is placed on the key instance detection (KID) problem, for which the package provides dedicated support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | milearn: A Python Package for Multi-Instance Machine Learning Zankov, Dmitry Polishchuk, Pavlo Sobieraj, Michal Barbatti, Mario Machine Learning We introduce milearn, a Python package for multi-instance learning (MIL) that follows the familiar scikit-learn fit/predict interface while providing a unified framework for both classical and neural-network-based MIL algorithms for regression and classification. The package also includes built-in hyperparameter optimization designed specifically for small MIL datasets, enabling robust model selection in data-scarce scenarios. We demonstrate the versatility of milearn across a broad range of synthetic MIL benchmark datasets, including digit classification and regression, molecular property prediction, and protein-protein interaction (PPI) prediction. Special emphasis is placed on the key instance detection (KID) problem, for which the package provides dedicated support. |
| title | milearn: A Python Package for Multi-Instance Machine Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.01287 |