An Elastic Gradient Boosting Decision Tree for Concept Drift Learning. - Kun Wang 050; Anjin Liu; Jie Lu 001; Guangquan Zhang 001; Li Xiong 002 (Australasian Conference on Artificial Intelligence, 2020)

Fast Switch Naïve Bayes to Avoid Redundant Update for Concept Drift Learning. - Anjin Liu; Guangquan Zhang 001; Kun Wang 050; Jie Lu 001 (IJCNN, 2020)

Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. - Kun Wang 050; Jie Lu 001; Anjin Liu; Yiliao Song; Li Xiong 002; Guangquan Zhang 001 (Neurocomputing, 2022)

An Augmented Learning Approach for Multiple Data Streams Under Concept Drift. - Kun Wang 050; Jie Lu 001; Anjin Liu; Guangquan Zhang 001 (AI (1), 2023)

TCR-M: A Topic Change Recognition-based Method for Data Stream Learning. - Kun Wang 050; Jie Lu 001; Anjin Liu; Guangquan Zhang 001 (KES, 2023)

Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift. - Kun Wang 050; Jie Lu 001; Anjin Liu; Guangquan Zhang 001; Li Xiong 002 (IEEE Trans. Cybern., 2023)

A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data. - Feng Gu; Jie Lu 001; Zhen Fang 001; Kun Wang 050; Guangquan Zhang 001 (CoRR, 2024)

A self-adaptive ensemble for user interest drift learning. - Kun Wang 050; Li Xiong 002; Anjin Liu; Guangquan Zhang 001; Jie Lu 001 (Neurocomputing, 2024)

TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation. - Kun Wang 050; Jie Lu 001; Anjin Liu; Guangquan Zhang 001 (IEEE Trans. Cybern., 2024)