CSIML: a cost-sensitive and iterative machine-learning method for small and imbalanced materials data sets
Materials science research benefits from the powerful machine-learning (ML) surrogate models, but it is also limited by the implicit requirement for sufficiently big and balanced data distribution for ML. In this paper, we propose a model to obtain more credible results for small and imbalanced mate...
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| Veröffentlicht in: | Chemistry letters 2024-05, Vol.53 (5) |
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| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Online-Zugang: | Volltext |
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