Correspondence
Mail: | Xu-Ying Liu | Tel: Fax: Office: URL: Email: | +86-25-52090xxx +86-25-52090xxx Room 420, CSE Buliding, JiuLongHu Campus http://cse.seu.edu.cn/PersonalPage/xyliu/index.htm (liuxy[at]seu.edu.cn) or (liuxy.gm[at]gmail.com) |
[Biography][Activity][Publication][Teaching][Students][Awards]
I am an Assistant Professor at School of Computer Science and Engineering of Southeast University, China since 2010. I received my B.Sc degree at Nanjing University of Aeronautics and Astronautics, China, M.Sc and Ph.D degree at Nanjing University, China in 2006 and 2010 respectively, both supervised by Prof. Zhi-Hua Zhou.
My research interests are machine learning and data mining, including new class discovery/augmented class learning, cost-sensitive learning, class imbalance learning, etc.
( CV )
Journal Reviewer | IEEE Transactions on Knowledge and Data Engineering |
| Data Mining and Knowledge Discovery |
| Pattern Recognition |
| Intelligent Data Analysis |
| Journal of Computer Science and Technology |
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PC Member | AAAI'19, IJCAI'17/11, KDD'15, ICDM'18/15, SDM'13, PAKDD'16/13, ACML'15/14/12, CIDM'14, ACM SAC'15, etc. |
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Non-PC Reviewer | PAKDD'11, ACM'10 |
Selected Publication / Full Publication List / Google Scholar
Book Chapters
X.-Y. Liu and Z.-H. Zhou. Ensemble methods for class imbalance learning (Chapter 4). In: H. He and Y. Ma (eds). Imbalanced Learning: Foundations, Algorithms, and Applications. IEEE Press Wiley, ISBN: 978-1118074626, 2013, 61-82.
X.-Y. Liu, S.-T. Wang, M.-L. Zhang. Transfer synthetic over-sampling for class-imbalance learning with limited minority class data. Frontiers of Computer Science, 2018, accepted.
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.
X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550. [code]
Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning. Computational Intelligence, 2010, 26(3): 232-257. [code]
Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2006, 18(1): 63-77. [code]
X. Ding, C.-C. Cao, X.-Y. Liu, etc. HSS-Bin: An unsupervised metagenomic binning method based on hybrid sequence feature recognition and spectral clustering. Current Bioinformatics, 2016, 11(3): 330-339.
S.-Y. Ding, X.-Y. Liu and M.-L. Zhang. Imbalanced augmented class learning with unlabeled data by label confidence propagation. In: Proceedings of the 18th IEEE International Conference on Data Mining (ICDM'18), Singapore, 2018. (full paper) [code]
M.-L. Zhang, B.-B. Zhou, X.-Y. Liu. Partial label learning via feature-aware disambiguation. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'16), San Francisco, CA, 2016, 1335-1344.
M.-L Zhang, Y.-K Li, X.-Y Liu. Towards class-imbalance aware multi-label learning. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15), 2015, Buenos Aires, Argentina, 2015, 4041-4047.
X.-Y. Liu, and Q.-Q. Li. Learning from combination of data chunks for multi-class imbalanced data. In: Proceedings of 2014 International Joint Conference on Neural Networks (IJCNN’14), Beijing, China, 2014.
X.-Y. Liu, Q.-Q. Li and Z.-H. Zhou. Learning imbalanced multi-class data with optimal dichotomy weights. In: Proceedings of the 13th IEEE International Conference on Data Mining (ICDM'13), Dallas, TX, 2013, 478-487.
X.-Y. Liu and Z.-H. Zhou. Learning with cost intervals. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'10), Washington, DC, 2010, pp.403-412. [code]
X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory under-sampling for class-imbalance learning. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.965-969. [code]
X.-Y. Liu and Z.-H. Zhou. The influence of class imbalance on cost-sensitive learning: An empirical study. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.970-974.
Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.567-572. [code]
Y. Yu, D.-C. Zhan, X.-Y. Liu, M. Li, and Z.-H. Zhou. Predicting future customers via ensembling gradually expanded trees. International Journal of Data Warehousing and Mining, 2007, 3(2): 12-21. Invited paper for the PAKDD'06 Data Mining Competition (Open Category) Grand Champion Team.
Artificial Intelligence (18/17/16/15/14 Autumn)
C++ Programming Language (14/13/12/11 Spring)
2015: Ying-Ying Jiang 江盈盈
2016: Si-Yu Ding 丁思宇
2016: Miao Zhu 朱淼
2016: Yin Wang 王引
2017: Na Li 李娜
2018: Xing-Yuan Liang 梁星元
2015: Qian-Qian Li 李倩倩 （硕士，毕业就职：）
2016: Jiang-Tao Liu 刘江涛 (硕士，毕业就职：南京电信)
2016: Lei Cao 曹磊 (硕士，毕业就职：腾讯)
2017: Sheng-Tao Wang 王胜涛 (硕士，毕业就职：新智云)
2017: Bin-Bin Zhou 周斌斌 (硕士，毕业就职：科大讯飞)
CCF Excellent Doctoral Dissertation Award (by China Computer Federation, 2010)
Best Paper Award of CAAI'12 (2007)
Microsoft Fellowship Award (2007)
Huaying Outstanding Youth (by Nanjing University, 2007)
China Outstanding Student Award (by IBM, in 2006 and 2007, respectively)
Grand Champion (open Category) of PAKDD'06 Data Mining Competition (2006)
Journal Papers
Conference Papers
Current Master Students
Alumni 毕业学生
