This repo contains 42 highly imbalanced datasets for the paper "A Hybrid Data-Level Ensemble (HD-Ensemble) for Highly Imbalance Learning". 28 of the datasets were extracted from KEEL, and 14 were manu
Classification algorithms face difficulties when one or more classes have limited training data. We are particularly interested in classification trees, due to their interpretability and flexibility.
Online imbalanced learning is an emerging topic that combines the challenges of class imbalance and concept drift. However, current works account for issues of class imbalance and concept drift. And o
