Name: Wu Qingyao
Introduction:
Ph.D., Associate Professor，Assistant Dean，Male
Email: qyw{at}scut.edu.cn
Office Address: School of Software Engineering, South China University of Technology, Higher Education Mega City, Guangzhou, P.R. China, 510006
Research Interest
I have wide research interests, including the following research topics: Multi-label learning, Multi-instance Learning, Deep Learning, Online learning, Transfer learning, Ensemble learning, Weak Supervised Learning, Semi-supervised learning, Network data learning.
Biography
I am currently an Associate Professor with the School of Software Engineering, South China University of Technology, China. I received the B.S. degree in software engineering from the South China University of Technology, and the M.S. and Ph.D. degrees in computer science from the Harbin Institute of Technology, China, in 2007, 2009, and 2013, respectively. I was a Post-Doctoral Research Fellow with the School of Computer Engineering, Nanyang Technological University, Singapore, from 2014 to 2015.
Selected Publications (*indicates corresponding author)
1. Journal Papers
【1】Yuguang Yan, Qingyao Wu*, Mingkui Tan, Michael Ng, Huaqing Min, Ivor Tsang, Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted
【2】Qingyao Wu, Hanrui Wu, Xiaoming Zhou, Mingkui Tan, Yonghui Xu, Yuguang Yan, Tianyong Hao, Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources, IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(7), pp.1494-1507, 2017 JULY
【3】Qingyao Wu, Mingkui Tan, Hengjie Song, Jian Chen, Michael K. Ng. ML-Forest: A Multi-label Tree Ensemble Method for Multi-Label Classification, IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(10), 2016, Oct (IF:2.067) (ML-Forest code) (RF-PCT code)
【4】Qingyao Wu*, Yunming Ye, Haijun Zhang, Tommy W.S.Chow, and Shen-Shyang Ho. ML-TREE: A Tree-Structure Based Approach to Multi-Label Learning, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 26(3): 430-443, 2015, Mar. (IF:4.37) (code)
【5】Qingyao Wu, Michael Ng, and Yunming Ye. Co-Transfer Learning Using Coupled Markov Chains with Restart, IEEE Intelligent Systems, 29(4), pp.26-33, 2014 (IF:1.93) (code) (dataset)
【6】Qingyao Wu, Yunming Ye, Yang Liu, and Michael K. Ng. SNP Selection and Classification of Genome-wide SNP Data Using Stratified Sampling Random Forests, IEEE Transactions on Nanobioscience, 11(3), 216-227, 2012 (IF:1.768)
【7】Xiaojun Chen, Joshua Z. Huang, Qingyao Wu*, Min Yang Subspace Weighting Co-Clustering of Gene Expression Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), DOI: 10.1109/TCBB.2017.2705686 (IF: 1.609)
【8】Xutao Li, Michael K. Ng, Gao Cong, Yunming Ye, and Qingyao Wu, MR-NTD: Manifold Regularization Nonnegative Tucker Decomposition for Tensor Data Dimension Reduction and Representation, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2016 (IF: 4.37)
【9】Yonghui Xu, Sinno Pan, Hui Xiong, Qingyao Wu, Yonghua Luo, Huaqing Min, Henjie Song, A Unified Framework for Metric Transfer Learning, IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(6),1158-1171, 2017 JUNE (IF:2.067)
【10】Qingyao Wu, Xiaoming Zhou, Yuguang Yan, Hanrui Wu, Huaqing Min, Online Transfer Learning by Leveraging Multiple Source Domains Knowledge and Information Systems, DOI: 10.1007/s10115-016-1021-1 (IF: 2.225)
【11】Qingyao Wu, Michael Ng, and Yunming Ye. Markov-MIML: A Markov Chain Based Multi-Instance Multi-Label Learning Algorithm, Knowledge and Information Systems, 37(1): 83-104, 2013 (IF:2.225) (code & dataset)
【12】Qingyao Wu, Michael Ng, Yunming Ye, Xutao Li, and Yan Li. Multi-Label Collective Classification via Markov Chain Based Learning Method, Knowledge-Based Systems, 63: 1-14, 2014 (IF:2.947) (code) (dataset)
【13】Qingyao Wu*, Yunming Ye, Haijun Zhang, Michael Ng, Xutao Li, Shen-Shyang Ho. ForesTexter: An Efficient Random Forest Algorithm for Imbalanced Text Categorization, Knowledge-Based Systems, 67: 105-116, 2014 (IF:2.947)
【14】Qingyao Wu, Mingkui Tan, Xutao Li, Huaqing Min, Ning Sun*, NMFE-SSCC: Non-negative matrix factorization ensemble for semi-supervised collective classification, Knowledge-Based Systems, 89 (2015): 160-172. (IF: 2.947)
【15】Yonghui Xu, Huaqing Min, Qingyao Wu*, Henjie Song, Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction, Scientific Reports, 7:41831, 2017 (IF: 5.228)
【16】Qingyao Wu, Yunming Ye, Shen-Shyang Ho and Shuigeng Zhou. Semi-Supervised Multi-label Collective Classification Ensemble for Functional Genomics, BMC Genomics, 15 (Suppl 9):S17, 2014 (IF:4.397) (code & dataset)
【17】Qingyao Wu, Zhenyu Wang, Chunshan Li, Yunming Ye, Yueping Li, and Ning Sun. Protein functional properties prediction in sparsely-label PPI networks through Regularized non-negative matrix factorization, BMC Systems Biology, 9 (Suppl 1):S9, 2015 (IF:2.853)
【18】Qingyao Wu, Yunming Ye, Michael Ng, Shen-Shyang Ho and Ruichao Shi. Collective prediction of protein functions from protein-protein interaction networks, BMC Bioinformatics, 15(S9), no. Suppl 2, 2014 (IF:3.024)
【19】Xutao Li, Yunming Ye, Michael Ng and Qingyao Wu*. MultiFacTV: Module Detection from Higher-order Time Series Biological Data, BMC Genomics, 14(S4), 2013 (IF:4.397)
【20】Qingyao Wu, Jian Chen, Shen-Shyang Ho, Xutao Li, Huaqing Min, Chao Han, Multi-Label Regularized Generative Model for Semi-Supervised Collective Classification in Large-Scale Networks, Big Data Research, 2 (4), 187-201, 2015
【21】Chao Han, Yunkun Tan, Jinhui Zhu, Yong Guo, Jian Chen, Qingyao Wu*, Online feature selection of Class Imbalance via PA algorithm Journal of Computer Science and Technology (JCST), 31(4): 673-682 (IF: 0.672)
【22】Chao Han, Jian Chen, Qingyao Wu*, Shuai Mu, Huaqing Min, Sparse Markov Chain based Semi-Supervised Multi-Instance Multi-Label Method for Protein Function Prediction, Journal of Bioinformatics and Computational Biology (JBCB), 13(05), 2015. (IF: 0.931)
【23】Yonghui Xu, Huaqing Min, Hengjie Song and Qingyao Wu*, Multi-Instance Multi-Label Distance Metric Learning for Genome-Wide Protein Function Prediction, Computational Biology and Chemistry, 11(5):891-902, 2015 (IF: 1.595)
【24】Yunming Ye, Qingyao Wu, Joshua Zhexue Huang, Michael K. Ng and Xutao Li. Stratified Sampling for Feature Subspace Selection in Random Forest for High Dimensional Data, Pattern Recognition (PR), 46(3): 769-787, 2013 (IF:2.584) (code)
【25】Michael K. Ng, Qingyao Wu, Chenyang Shen, A fast Markov chain based algorithm for MIML learning, Neurocomputing, 216 (5), 763-777, 2016
【26】Yunming Ye, Qingyao Wu, K.P.Chow, Lucas C.K. Hui, and S.M. Yiu. Unknown Chinese Word Extraction based on Variety of Overlapping Strings, Information Processing and Management (IPM), 49(2): 497-512, 2013 (IF:1.069) (dataset)
【27】Tianyong Hao, Wenxiu Xie, Qingyao Wu, Heng Weng, Yingying Qu. Leveraging Question Target Word Features through Semantic Relation Expansion for Answer Type Classification. Knowledge-Based Systems. Accepted in 2017 and to be appear
【28】Thanh-Tung Nguyen, Joshua Z. Huang, Qingyao Wu, Thuy T. Nguyen and Mark J. Li. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests BMC Genomics, 16(Suppl 2):S5, 2015 (IF:4.397)
2. Conference Papers
【1】Yuguang Yan, Wen Li, Michael Ng, Mingkui Tan, Qingyao Wu*, Hanrui Wu, Huaqing Min, Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation, International Joint Conference on Artificial Intelligence (IJCAI-2017), 2017
【2】Chao Han#, Qingyao Wu#, Jiezhang Cao, Michael K. Ng, Mingkui Tan, Jian Chen, Tensor based Relations Ranking and Collective Classification for Multi-relational Data, IEEE International Conference on Data Mining (ICDM 2017), 2017 (# co-first authors)
【3】Xiaojun Chen, Guowen Yuan, JianZhe Zhang, Joshua Zhexue Huang, Qingyao Wu*, Local PurTree Subspace Spectral Clustering for Customer Transaction Data, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), 2017
【4】Xiaojun Chen, Guowen Yuan, JianZhe Zhang, Joshua Zhexue Huang, Qingyao Wu, A Self-Balanced Min-Cut Algorithm for Image Clustering, IEEE International Conference on Computer Vision (ICCV 2017), 2017
【5】Yuguang Yan, Qingyao Wu*, Mingkui Tan, Huaqing Min, Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers, ECCV-2016 workshop on TASK Transferring and Adapting Source Knowledge in Computer Vision, 2016 (Honorable Mention Paper Award)
【6】Feng Wu, Qiong Liu*, Tianyong Hao, Xiaojun Chen, and Qingyao Wu*, Online Multi-Instance Multi-Label Learning for Protein Function Prediction, IEEE BIBM-2016
【7】Yongxin Liao, Shenxi Yuan, Jian Chen, Qingyao Wu* and Bin Li, Joint Classification with Heterogeneous labels using random walk with dynamic label propagation, PAKDD-2016, 2016
【8】Ruichao Shi, Qingyao Wu*, Yunming Ye, and Shen-Shyang Ho. A Generative Model with Network Regularization for Semi-Supervised Collective Classification, SDM-2014 (code & dataset)
【9】Michael Ng, Qingyao Wu and Yunming Ye. Co-Transfer Learning via Joint Transition Probability Graph Based Method. SIGKDD-2012 Workshop on CDKD, pp.1-9, 2012 (Selected Best Paper to IEEE IS Special Issue) (code) (dataset)
【10】Qingyao Wu, Yunming Ye, Xiaofeng Zhang and Shen-Shyang Ho. Cluster Tree based Multi-Label Classification for Protein Function Prediction. IEEE BIBM-2013,
【11】Qingyao Wu, Yunming Ye, Yang Liu, and Michael Ng. Stratified Random Forest for Genome-wide Association Study, IEEE BIBM-2012, pp.10-15, 2011
【12】Xutao Li, Yunming Ye, Qingyao Wuand Michael Ng. MultiFacTV: Finding Modules from Higher-order Gene Expression Profiles with Time Dimension, IEEE BIBM-2012, pp. 53-58, 2012
