Evidential Undersampling Approach for Imbalanced Datasets with Class-Overlapping and Noise
Résumé
The class imbalance issue involves many real world domains such as fraud detection, medical diagnosis, intrusion detection, etc. Most classification algorithms tend to perform poorly when the training dataset is class-imbalanced. This problem gets more challenging in the presence of other factors such class-overlapping and noise. Among many methods, undersampling is a simple and efficient approach which rebalances the imbalanced dataset by removing majority samples. In this paper, we propose a novel method named Evidential Undersampling (EVUS), which is a re-sampling approach based on the theory of evidence. To avoid removing meaningful samples, each majority object is assigned a soft evidential label to gain more information about its location, then majority samples which are considered ambiguous or noisy by our framework, are eliminated from the training set. The conducted results with CART and SVM show that our proposal outperformed other well-known undersampling methods according to the AUC metric.
Domaines
Intelligence artificielle [cs.AI]Origine | Fichiers produits par l'(les) auteur(s) |
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