Resample-based Ensemble Framework for Drifting Imbalanced Data Streams

Zhang, Hang and Liu, Weike and Wang, Shuo and Shan, Jicheng and Liu, Qingbao (2019) Resample-based Ensemble Framework for Drifting Imbalanced Data Streams. IEEE Access, 7 (2019). ISSN 2169-3536

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Abstract

Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI). The ensemble framework consists of a long-term static classifier to handle gradual and multiple dynamic classifiers to handle sudden concept drift. The weights of the ensemble classifier are adjusted from two aspects. First, a time-decayed strategy decreases the weights of the dynamic classifiers to make the ensemble classifier focus more on the new concept of the data stream. Second, a novel reinforcement mechanism is proposed to increase the weights of the base classifiers that perform better on the minority class and decrease the weights of the classifiers that perform worse. A resampling buffer is used for storing instances of the minority class to balance the imbalanced distribution over time. In our experiment, we compare the proposed method with other state-of-the-art algorithms on both real-world and synthetic data streams. The results show that the proposed method achieves the best performance in terms of both the Prequential AUC and accuracy.

Item Type: Article
Additional Information: (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Uncontrolled Keywords: Online ensemble learning, resample learning ,reinforcement, concept drift, class imbalance
Subjects: G700 Artificial Intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
REF UoA Output Collections > REF2021 UoA11: Computer Science and Informatics
Depositing User: Shuo Wang
Date Deposited: 09 May 2019 06:15
Last Modified: 12 Jul 2019 09:21
URI: http://www.open-access.bcu.ac.uk/id/eprint/7431

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