Short Biography: I am now a lecturer at the College of Information Science and Technology, Jinan University.
I received the Ph.D. degree from the School of Informatics, Xiamen University, Xiamen, China, in 2020, supervised by Prof. Shaozi Li. I was a visiting scholar at City University of Hong Kong, supervised by Prof. Kay Chen Tan. My research interests include machine learning, data mining, and human-computer interaction. I am generally interested in design, analysis, and implementation of algorithms for scientific computing problems.
Selected Publications (More Details in Google Scholar Link)
- G. Du, J. Zhang, M. Jiang, J. Long, Y. Lin, S. Li, K. C. Tan. Graph-based class-imbalance learning with label enhancement. IEEE Transactions on Neural Networks and Learning Systems (Early Access), 2021.
- J. Zhang, S. Li, M. Jiang, K. C. Tan. Learning from weakly labeled data based on manifold regularized sparse model. IEEE Transactions on Cybernetics (Early Access), 2020. [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, 2021, 32(9): 3971-3984.
- G. Du, J. Zhang, F. Ma, M. Zhao, Y. Lin, S. Li. Towards graph-based class-imbalance learning for hospital readmission. Expert Systems with Applications, 2021, 176: 114791.
- 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] [report] [poster]
- 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.
- 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.
- AI Principles (for undergraduate students), Fall, 2021
- Computer Fundamentals (for undergraduate students, in English), Fall, 2021