I received the Ph.D. degree from the Artificial Intelligence Department, Xiamen University, Xiamen, China, in 2020, under the supervision of Prof. Shaozi Li. In 2019, I visited Kay Chen Tan’s group in the City University of Hong Kong for three months. [Curriculum Vitae]
Mailing Address: Room 304, Scientific Research Building, Haiyun Campus, Xiamen 361005, P. R. China
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…
- 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]
- 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.
- 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.
- 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.
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