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Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks
dc.contributor.author | Ramos-López, Darío | |
dc.contributor.author | Maldonado, Ana D. | |
dc.date.accessioned | 2021-01-18T09:33:56Z | |
dc.date.available | 2021-01-18T09:33:56Z | |
dc.date.issued | 2021-01-13 | |
dc.identifier.issn | 2227-7390 | |
dc.identifier.uri | http://hdl.handle.net/10835/9318 | |
dc.description.abstract | 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 others are to be avoided completely. Therefore, a cost-sensitive variable selection procedure for building a Bayesian network classifier is proposed. In it, a flexible validation metric (cost/loss function) encoding the impact of the different classification errors is employed. Thus, the model is learned to optimize the a priori specified cost function. The proposed approach was applied to forecasting an air quality index using current levels of air pollutants and climatic variables from a highly imbalanced dataset. For this problem, the method yielded better results than other standard validation metrics in the less frequent class states. The possibility of fine-tuning the objective validation function can improve the prediction quality in imbalanced data or when asymmetric misclassification costs have to be considered. | es_ES |
dc.language.iso | en | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | multi-class classification | es_ES |
dc.subject | imbalanced data | es_ES |
dc.subject | Bayesian networks | es_ES |
dc.subject | variable selection | es_ES |
dc.title | Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2227-7390/9/2/156 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |