I received the Ph.D. degree from the Artificial Intelligence Department, Xiamen University, Xiamen, China, in 2020, under the supervision of Prof. Shaozi Li, and received the M.S. degree from the School of Computer Science, Minnan Normal University, Zhangzhou, China, in 2016, under the supervision of Prof. Menglei Lin and Prof. Yaojin Lin. In 2019, I visited Kay Chen Tan’s group in the City University of Hong Kong for three months. [Curriculum Vitae]
Research Interests: I am broadly interested in machine learning and data mining. I am generally interested in design, analysis, and implementation of algorithms for scientific computing problems. Now I am working on weak label learning, data fusion, feature selection, knowledge graph, and some AI applications in medicine, such as TCM health management, drug discovery, hospital readmission, and autism spectrum disorder.
https://orcid.org/0000-0002-6079-2818Selected Publications [Google Scholar Citations]
- J. Zhang, S. Li, M. Jiang, K. C. Tan. Learning from weakly labeled data based on manifold regularized sparse model. IEEE Transactions on Cybernetics, in press. [code]
- Z.-A. Huang, J. Zhang, Z. Zhu, E. Q. Wu, K. C. Tan. Identification of autistic risk candidate genes and toxic chemicals via multi-label learning. IEEE Transactions on Neural Networks and Learning Systems, in press.
- G. Du, J. Zhang, S. Li, C. Li. Learning from class-imbalance and heterogeneous data for 30-day hospital readmission. Neurocomputing, 2021, 420: 27-35.
- J. Zhang, Y. Lin, M. Jiang, S. Li, Y. Tang, K. C. Tan. Multi-label feature selection via global relevance and redundancy optimization. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI’20), Yokohama, Japan, 2020, pp. 2512–2518. [code]
- G. Du, J. Zhang, Z. Luo, F. Ma, L. Ma, S. Li. Joint imbalanced classification and feature selection for hospital readmissions. Knowledge-Based Systems, 2020, 200: 106020.
- J. Zhang, Z. Luo, C. Li, C. Zhou, S. Li. Manifold regularized discriminative feature selection for multi-label learning. Pattern Recognition, 2019, 95: 136-150. [code]
- J. Zhang, C. Li, Z. Sun, Z. Luo, C. Zhou, S. Li. Towards a unified multi-source-based optimization framework for multi-label learning. Applied Soft Computing, 2019, 76: 425-435.
- Z. Sun, J. Zhang, L. Dai, C. Li, C. Zhou, J. Xin, S. Li. Mutual information based multi-label feature selection via constrained convex optimization. Neurocomputing, 2019, 329: 447-456.
- J. Zhang, C. Li, D. Cao, Y. Lin, S. Su, L. Dai, S. Li. Multi-label learning with label-specific features by resolving label correlations. Knowledge-Based Systems, 2018, 159: 148-157.
- J. Zhang, C. Li, Y. Lin, Y. Shao, S. Li. Computational drug repositioning using collaborative filtering via multi-source fusion. Expert Systems with Applications, 2017, 84: 281-289.
- J. Zhang, Y. Lin, M. Lin, J. Liu. An effective collaborative filtering algorithm based on user preference clustering. Applied Intelligence, 2016, 45 (2): 230-240.
- Y. Lin, Q. Hu, J. Zhang, X. Wu. Multi-label feature selection with streaming labels. Information Sciences, 2016, 372: 256-275.
Professional Activities
- Journal Reviewer: IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, Information Sciences, Knowledge-Based Systems, Applied Intelligence, PLOS ONE
Web Page Link
- KDnuggets: Machine Learning, Data Science, Big Data, Analytics, AI
- For reference purpose, there are some reasonable public CS conference ranking lists provided by China Computer Federation: Ref1, Ref2.