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

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 othe...

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Bibliographic Details
Published in:Mathematics (Basel) 2021-01, Vol.9 (2), p.156
Main Authors: Ramos-López, Darío, Maldonado, Ana D.
Format: Article
Language:English
Subjects:
Accuracy
Air pollution
Air quality
Algorithms
Bayesian analysis
Bayesian networks
Classification
Cost function
Datasets
Feature selection
imbalanced data
multi-class classification
Neural networks
Pollutants
Probability distribution
Random variables
variable selection
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9020156