Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data
Abstract Background Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature select...
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| Автори: | , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
BMC
2020-03-01
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| Серія: | BMC Bioinformatics |
| Предмети: | |
| Онлайн доступ: | http://link.springer.com/article/10.1186/s12859-020-3411-3 |
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