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

Darío Ramos-López and Ana D. Maldonado
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Darío Ramos-López: Department of Applied Mathematics, Materials Science and Engineering, and Electronic Technology, Rey Juan Carlos University, 28933 Móstoles, Spain
Ana D. Maldonado: Department of Mathematics, University of Almería, 04120 Almería, Spain

Mathematics, 2021, vol. 9, issue 2, 1-15

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.

Keywords: multi-class classification; imbalanced data; Bayesian networks; variable selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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