Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks

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...

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Dades bibliogràfiques
Autors principals: Ramos-López, Darío, Maldonado, Ana D.
Format: info:eu-repo/semantics/article
Idioma:English
Publicat: MDPI 2021
Matèries:
multi-class classification
imbalanced data
Bayesian networks
variable selection
Accés en línia:http://hdl.handle.net/10835/9318

Internet

http://hdl.handle.net/10835/9318

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