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dc.contributor.authorRamos-López, Darío
dc.contributor.authorMaldonado, Ana D.
dc.date.accessioned2021-01-18T09:33:56Z
dc.date.available2021-01-18T09:33:56Z
dc.date.issued2021-01-13
dc.identifier.issn2227-7390
dc.identifier.urihttp://hdl.handle.net/10835/9318
dc.description.abstractMulti-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.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectmulti-class classificationes_ES
dc.subjectimbalanced dataes_ES
dc.subjectBayesian networkses_ES
dc.subjectvariable selectiones_ES
dc.titleCost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/2227-7390/9/2/156es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional