Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks
Multi-class classification in imbalanced datasets is a challenging problem. In these cases, common validation metrics (such as accuracy or recall) are often not suitable. In many of these problems, often real-world problems related to health, some classification errors may be tolerated, whereas othe...
| Huvudupphovsmän: | , |
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| Materialtyp: | info:eu-repo/semantics/article |
| Språk: | English |
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MDPI
2021
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| Länkar: | http://hdl.handle.net/10835/9318 |