Publications

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Recent Journal Papers

• Kun Wang, Jie Lu, Anjin Liu, “Adaptive Information Fusion-Based Concept Drift Learning for Evolving Multiple Data Streams”, IEEE Transactions on Knowledge and Data Engineering, 2025. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “TS-DM: A time segmentation-based data stream learning method for concept drift adaptation”, IEEE Transactions on Cybernetics, 2024. [Link]

• Kun Wang,Li Xiong, Rudan Xue, “Real-time data stream learning for emergency decision-making under uncertainty”, Physica A: Statistical Mechanics and its Applications, 2024. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “Evolving gradient boost: A pruning scheme based on loss improvement ratio for learning under concept drift”, IEEE Transactions on Cybernetics, 2023. [Link]

• Kun Wang, Li Xiong, Anjin Liu, Guangquan Zhang, Jie Lu, “A self-adaptive ensemble for user interest drift learning”, Neurocomputing, 2023. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Yiliao Song, Guangquan Zhang, Li Xiong, “Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation”, Neurocomputing, 2022. [Link]

Recent Conference Papers

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, An adaptive stacking method for multiple data streams learning under concept drift, FLINS-ISKE 2024. [Link]

• Bin Zhang, Jie Lu, Kun Wang, Guangquan Zhang, ML4MDS: A machine learning platform for multiple data stream, FLINS-ISKE 2024. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “TCR-M: A topic change recognition-based method for data stream learning”, KES 2023. [Link]

• Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, “An augmented learning approach for multiple data streams under concept drift”, AJCAI 2023. [Link]

• Kun Wang, Anjin Liu, Jie Lu, Guangquan Zhang, Li Xiong, “An elastic gradient boosting decision tree for concept drift learning”, AJCAI 2020. [Link]