Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classification
This addresses the problem of classifying binary, non-stationary, and imbalanced data streams, but it appears incremental as it builds on existing ensemble techniques with a specific distance metric.
The paper tackles imbalanced data stream classification by proposing a Hellinger Distance Weighted Ensemble (HDWE) method that prunes the ensemble using Hellinger Distance, and experimental results demonstrate its usefulness compared to state-of-the-art methods.
The imbalanced data classification remains a vital problem. The key is to find such methods that classify both the minority and majority class correctly. The paper presents the classifier ensemble for classifying binary, non-stationary and imbalanced data streams where the Hellinger Distance is used to prune the ensemble. The paper includes an experimental evaluation of the method based on the conducted experiments. The first one checks the impact of the base classifier type on the quality of the classification. In the second experiment, the Hellinger Distance Weighted Ensemble (HDWE) method is compared to selected state-of-the-art methods using a statistical test with two base classifiers. The method was profoundly tested based on many imbalanced data streams and obtained results proved the HDWE method's usefulness.