Multi-window based ensemble learning for classification of imbalanced streaming data

Abstract

Imbalanced streaming data is widely existed in real world and has attracted much attention in recent years. Most studies focus on either imbalance data or streaming data; however, both imbalance data and streaming data are always accompanied in practice. In this paper, we propose a multi-window based ensemble learning (MWEL as short) method for the classification of imbalanced streaming data. Three types of windows are defined to store the current batch of instances, the latest minority instances and the ensemble classifier. The ensemble classifier consists of a set of latest sub-classifiers, and instances each sub-classifier trained on respectively. All sub-classifiers are weighted before predicting new arriving instance’s class labels and new sub-classifiers are trained if a precision is below a threshold. Extensive experiments on synthetic datasets and real world datasets demonstrate that the new approach can efficiently and efficiently classify imbalanced streaming data and outperform existing approaches.