Class-specific weighted broad learning system for imbalanced heartbeat classification

Abstract

Accurate classification of heartbeats is essential for the treatment of cardiac arrhythmia. In real-world applications, many existing methods suffer from the imbalance between heartbeat classes since the number of normal heartbeats heavily outnumbers that of abnormal heartbeats. To address the class imbalance problem effectively, this study proposes a novel class-specific weighted broad learning system (CSWBLS) based on the broad learning system (BLS). The proposed CSWBLS constructs the least squares error term by class and uses weights to constrain the contribution of each class to the model. The weights are computed by employing class distribution and fine-tuning with preset scale factors. Minority classes are allocated higher weights to increase their contributions. Furthermore, the proposed CSWBLS can be quickly remodeled by our newly developed incremental learning algorithms when additional nodes are added. The proposed CSWBLS improves the generalization performance for imbalanced classification tasks while inheriting the fast learning efficiency compared to traditional BLS. To validate the proposed method, we conducted experiments on five imbalanced heartbeat datasets obtained from the MIT-BIH Arrhythmia Database. The results show that the overall accuracy and G-mean of our method exceed 99% and 97%, respectively, outperforming traditional BLS and some state-of-the-art methods.