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

Mathematics

Darío Ramos-López¹  AnaD. Maldonado² 

[1] Department of Applied Mathematics, Materials Science and Engineering, and Electronic Technology, Rey Juan Carlos University, 28933 Móstoles, Spain;Department of Mathematics, University of Almería, 04120 Almería, Spain;

关键词: multi-class classification;    imbalanced data;    Bayesian networks;    variable selection;   

DOI  :  10.3390/math9020156

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown