个人简历

Currently, I am a professor at the PALM Group, School of Computer Science and Engineering, Southeast University. Before joining Southeast University, I worked as an assistant professor (2007.10~2010.5) at the College of Computer and Information Engineering, Hohai University.
I received my B.Sc., M.Sc., and Ph.D. degrees in computer science all from Department of Computer Science & Technology, Nanjing University, China, in 2001, 2004 and 2007 respectively. I was also a member of LAMDA group led by my supervisor Prof. Zhi-Hua Zhou.

研究领域

My research interests mainly include machine learning and data mining.

近期论文

M.-L. Zhang, J.-P. Fang. Partial multi-label learning via credible label elicitation. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press. [Conference version] [code]
M.-L. Zhang, Q.-W. Zhang, J.-P. Fang, Y.-K. Li, X. Geng. Leveraging implicit relative labeling-importance information for effective multi-label learning. IEEE Transactions on Knowledge and Data Engineering, in press. [Conference version] [code]
B.-B Jia, M.-L. Zhang. Multi-dimensional classification via kNN feature augmentation. Pattern Recognition, in press. [Conference version] [code]
Y.-P. Sun, M.-L. Zhang. Compositional metric learning for multi-label classification. Frontiers of Computer Science, in press. [code]
Y. Zhang, Y. Wang, X.-Y. Liu, S. Mi, M.-L. Zhang. Large-scale multi-label classification using unknown streaming images. Pattern Recognition, in press.
M. Huang, F. Zhuang, X. Zhang, X. Ao, Z. Niu, M.-L. Zhang, Q. He. Supervised representation learning for multi-label classification. Machine Learning, 2019, 108(5): 747-763.
D. Zhou, Z. Zhang, M.-L. Zhang, Y. He. Weakly supervised POS tagging without disambiguation. ACM Transactions on Asian and Low-Resource Language Information Processing, 2018, 17(4): Article 35.
M.-L. Zhang, Y.-K. Li, X.-Y. Liu, X. Geng. Binary relevance for multi-label learning: An overview. Frontiers of Computer Science, 2018, 12(2): 191-202.
M.-L. Zhang, F. Yu, C.-Z. Tang. Disambiguation-free partial label learning. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2155-2167. [Conference version] [data] [code]
F. Yu, M.-L. Zhang. Maximum margin partial label learning. Machine Learning, 2017, 106(4): 573-593. [Conference version] [data] [code]
M.-L. Zhang, L. Wu. LIFT: Multi-label learning with label-specific features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107-120. [Conference version] [code]
M.-L. Zhang, Z.-H. Zhou. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837. [Longer version]
M.-L. Zhang, Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98-129. [Conference version] [code]
Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176(1): 2291-2320. [code] (CORR abs/1005.1545)
M.-L. Zhang, Z.-H. Zhou. CoTrade: Confident co-training with data editing. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(6): 1612-1626. [code]
M.-L. Zhang, J. M. Peña, V. Robles. Feature selection for multi-label naive bayes classification. Information Sciences, 2009, 179(19): 3218-3229. [code]
M.-L. Zhang, Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68. [code]
M.-L. Zhang, Z.-J. Wang. MIMLRBF: RBF neural networks for multi-instance multi-label learning. Neurocomputing, 2009, 72(16-18): 3951-3956. [code] [image data] [retuers data]
M.-L. Zhang. ML-RBF: RBF neural networks for multi-label learning. Neural Processing Letters, 2009, 29(2): 61-74. [code]
M.-L. Zhang, Z.-H. Zhou. ML-kNN: a lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40(7): 2038-2048. [code] [Yeast data] [image data] [Yahoo data: original version preprocessed version]