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. During my doctoral study, I visited Kay Chen Tan’s group in the City University of Hong Kong for three months. [Curriculum Vitae]
Email: zhangjia_gl@163.com
Mailing Address: Room 304, Scientific Research Building, Haiyun Campus of Xiamen University, Xiamen 361005, P. R. China
Research Interests
I am broadly interested in machine learning, data mining, and artificial intelligence. I am generally interested in design, analysis, and implementation of algorithms for scientific computing problems. Now I am working on:
- Classification: multi-label learning, weakly supervised learning, class-imbalance learning
- Feature selection and sparse learning
- AI applications in medicine like TCM health management, drug discovery, hospital readmission, and autism spectrum disorder
- Others: data fusion, graph knowledge, collaborative filtering…
Selected 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, 2020, in press. [code]
- 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]
- Z.-A. Huang, J. Zhang, Z. Zhu, E. 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, 2020, in press.
- 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.
- G. Du, J. Zhang, S. Li, C. Li. Learning from class-imbalance and heterogeneous data for 30-day hospital readmission. Neurocomputing, 2020, in press.
- L. Dai, G. Du, J. Zhang, C. Li, R. Wei, S. Li. Joint multi-label classification and feature selection based on deep canonical correlation analysis. Concurrency and Computation: Practice and Experience, 2020, in press.
- J. Liu, Y. Li, W. Weng, J. Zhang, B. Chen, S. Wu. Feature selection for multi-label learning with streaming label. Neurocomputing, 2020, 387: 268-278.
- Y. Lin, J. Li, A. Tan, J. Zhang. Granular matrix-based knowledge reductions of formal fuzzy contexts. International Journal of Machine Learning and Cybernetics, 2020, 11 (3): 643–656.
- Y. Lin, J. Li, S. Liao, J. Zhang, J. Liu. Reduction of fuzzy-crisp concept lattice based on order-class matrix. Journal of Intelligent & Fuzzy Systems, 2020, in press.
- 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.
- L. Dai, J. Zhang, C. Li, C. Zhou, S. Li. Multi‐label feature selection with application to TCM state identification. Concurrency and Computation: Practice and Experience, 2019, 31(23): e4634.
- 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. Liu, Y. Lin, M. Lin, S. Wu, J. Zhang. Feature selection based on quality of information. Neurocomputing, 2017, 225: 11-22.
- 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, Applied Intelligence, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, Information Sciences, Knowledge-Based Systems
Web Page Link
- KDnuggets: Machine Learning, Data Science, Big Data, Analytics, AI
- For reference purpose, CCF provides a public CS journal/conference ranking list. [link]
- [Resource] Multi-label data sets are available at the KDIS, Mulan Library, Sourceforge Network, Lambda, and Extreme Classification Repository.