ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets. (arXiv:2203.10769v3 [cs.LG] UPDATED)

Nowadays, many classification algorithms have been applied to various
industries to help them work out their problems met in real-life scenarios.
However, in many binary classification tasks, samples in the minority class
only make up a small part of all instances, which leads to the datasets we get
usually suffer from high imbalance ratio. Existing models sometimes treat
minority classes as noise or ignore them as outliers encountering data skewing.
In order to solve this problem, we propose a bagging …

anomaly arxiv datasets ensemble learning lg scoring