A novel under-sampling algorithm based on Iterative-Partitioning Filters for imbalanced classification

Many real-world datasets suffer from the problem of class imbalance, i.e., they have a minority class being only a small portion of the whole dataset. Under-sampling techniques, e.g., EasyEnsemble (EE), present an efficient approach to imbalanced classification problems. However, imbalance is not the only factor that harms the performance of conventional classifiers. The presence of noises is another issue that really complicates the process of classifier learning. This paper presents a new noise-filtered under-sampling algorithm by incorporating an Iterative-Partitioning Filter (IPF) into EE, named EE-IPF for short. IPF can remove noises from both majority and minority classes. Comprehensive experiments are performed to test its performance via eleven commonly-used benchmark datasets. The results show its outstanding performance in terms of popular ly-used metrics for imbalanced classification.