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• W3120105084 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." @default.
• W3120105084 created "2021-01-18" @default.
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• W3120105084 date "2021-01-13" @default.
• W3120105084 modified "2023-09-26" @default.
• W3120105084 title "Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks" @default.
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• W3120105084 doi "https://doi.org/10.3390/math9020156" @default.
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