successful techniques to handle imbalanced data is undersampling. Experiments demonstrate that cluster-based undersampling … improves over random undersampling in many cases. In this paper, we propose three new boosting approaches to improve the … performance of cluster-based undersampling technique: (i) inject unlabeled data into training data for improved clustering; (ii …

A clustering-based undersampling (CUS) and distance-based near-miss method are widely used in current imbalanced …. To overcome these drawbacks, this study proposes an undersampling method combining distance measurement and majority … clustering and distance-based imbalance learning model (CDEILM). This algorithm combines distance-based undersampling, feature …

), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific … practice (Ling and Li, 1998). Results show that under-sampling can lead to improved prediction accuracy, especially when …