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中文
Jiang Liangxiao

Professor
Doctoral Supervisor
Master Tutor


Gender : Male
Degree : Doctoral Degree
School/Department : School of Computer Science
Email :
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Personal Profile

Liangxiao Jiang is currently a professor at the School of Computer Science of China University of Geosciences. His main research interests include data mining and machine learning. He has published more than 100 papers in renowned international journals and conferences such as TKDETNNLS, TSMCSCIS, PR, INS, KAIS, FCS, IJIS, KBS, EAAI, ESWA, NEUCOM, NCA, PRL, JETAI, JIIS, IJPRAI, APIN, INFFUS, ASOC, IJAIT, IJMLC, IJCA, ICML, IJCAI, AAAI, ICDM, DASFAA, ICTAI, PRICAI, ICANN, IJCNN, ICIC, and ADMA. Besides, he serves/ed as EB/PC Members of several renowned international journals and conferences such as JIFS, Mathematics, SP, JIS, OJCST, OCS, CEJCS, AISS, ICML, IJCAI, AAAI, NeurIPS, UAI, PAKDD, PRICAI, ICANN, ICLR, and ICBK.

Career:

2011/12-Present,  Professor at China University of Geosciences

2009/07-2011/11, Associate Professor at China University of Geosciences

2006/07-2009/06, Lecturer at China University of Geosciences

2004/07-2006/06, Assistant at China University of Geosciences

Education:

2006/09-2009/06, Ph.D. student at China University of Geosciences

2001/09-2004/06, M.Sc. student at China University of Geosciences

1997/09-2001/06, B.Sc. student at China University of Geosciences

Interest:

Data Mining Tasks: classification, ranking, class probability estimation, clustering, regression, distance measure, feature selection, defect detection

Machine Learning Techniques: Bayesian learning, decision tree learning, nearest neighbor learning, cost-sensitive learning, crowdsourcing learning, deep learning

Course:

Machine Learning (For Undergraduate Students)

Data Mining and Machine Learning (For Graduate Students)

Journal Article: (*Corresponding authors) 

Conference Paper: (*Corresponding authors) 

  • H. Zhang, L. Jiang*, and W. Xu. Multiple Noisy Label Distribution Propagation for Crowdsourcing. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 1473-1479.

  • L. Yu, L. Jiang*, L. Zhang, and D. Wang. Weight Adjusted Naive Bayes. In: Proceedings of the 30th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2018, pp. 825-831.

  • C. Qiu, L. Jiang*, and Z. Cai. Using Differential Evolution to Estimate Labeler Quality for Crowdsourcing. In: Proceedings of the 15th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018, LNAI 11013, pp. 165-173.

  • H. Zhang, L. Jiang*, and W. Xu. Differential Evolution-Based Weighted Majority Voting for Crowdsourcing. In: Proceedings of the 15th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018, LNAI 11013, pp. 228-236.

  • L. Zhang, L. Jiang*, and C. Li. C4.5 or Naive Bayes: A Discriminative Model Selection Approach. In: Proceedings of the 25th International Conference on Artificial Neural Networks, ICANN 2016, LNCS 9886, pp. 419-426.

  • C. Qiu, L. Jiang*, and G. Kong. A Differential Evolution-Based Method for Class-Imbalanced Cost-Sensitive Learning. In: Proceedings of the 2015 International Joint Conference on Neural Networks, IJCNN 2015, pp. 1-8. 

  • S. Wang, L. Jiang*, and C. Li. A CFS-based Feature Weighting Approach to Naive Bayes Text Classifiers. In: Proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, LNCS 8681, pp. 555-562.

  • C. Li*, L. Jiang, H. Li, and S. Wang. Attribute Weighted Value Difference Metric. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013, pp. 575-580. 

  • L. Jiang*, C. Li, Z. Cai, and H. Zhang. Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013, pp. 512-517. 

  • L. Jiang*, C. Li, J. Wu, and J. Zhu. A Combined Classification Algorithm Based on C4.5 and NB. In: Proceedings of the 3rd International Symposium on Intelligence Computation and Applications, ISICA 2008, LNCS 5370, pp. 350-359.

  • L. Jiang*, H. Zhang, D. Wang, and Z. Cai. Learning Locally Weighted C4.4 for Class Probability Estimation. In: Proceedings of the 10th International Conference on Discovery Science, DS 2007, LNAI 4755, pp. 104-115.

  • D. Wang* and L. Jiang. An Improved Attribute Selection Measure for Decision Tree Induction. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Volume 4, pp. 654-658.

  • L. Jiang*, Z. Cai, D. Wang, and S. Jiang. Survey of Improving K-Nearest-Neighbor for Classification. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Volume 1, pp. 679-683.

  • L. Jiang*, D. Wang, and Z. Cai. Scaling Up the Accuracy of Bayesian Network Classifiers by M-Estimate. In: Proceedings of the 3rd International Conference on Intelligent Computing, ICIC 207, LNAI 4682, pp. 475-484.

  • Z. Cai*, D. Wang, and L. Jiang. K-Distributions: A New Algorithm for Clustering Categorical Data. In: Proceedings of the 3rd International Conference on Intelligent Computing, ICIC 2007, LNAI 4682, pp. 436-443.

  • L. Jiang*, D. Wang, Z. Cai, and X. Yan. Survey of Improving Naive Bayes for Classification. In: Proceedings of the 3rd International Conference on Advanced Data Mining and Applications, ADMA 2007, LNAI 4632, pp. 134-145.

  • L. Jiang*, H. Zhang, and Z. Cai. Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted. In: Proceedings of the 3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006, LNAI 4223, pp. 365-368.

  • L. Jiang* and H. Zhang. Weightily Averaged One-Dependence Estimators. In: Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, LNAI 4099, pp. 970-974. 

  • C. Li* and L. Jiang. Using Locally Weighted Learning to Improve SMOreg for Regression. In: Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, LNAI 4099, pp. 375-384.

  • L. Jiang* and H. Zhang. Lazy Averaged One-Dependence Estimators. In: Proceedings of the 19th Canadian Conference on Artificial Intelligence, CAI 2006, LNAI 4013, pp. 515-525.

  • L. Jiang* and H. Zhang. Learning Naive Bayes for Probability Estimation by Feature Selection. In: Proceedings of the 19th Canadian Conference on Artificial Intelligence, CAI 2006, LNAI 4013, pp. 503-514. 

  • L. Jiang* and H. Zhang. Learning Instance Greedily Cloning Naive Bayes for Ranking. In: Proceedings of the 5th IEEE International Conference on Data Mining, ICDM 2005, pp. 202-209. 

  • L. Jiang* and Y. Guo. Learning Lazy Naive Bayesian Classifiers for Ranking. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2005, pp. 412-416.

  • H. Zhang*, L. Jiang, and J. Su. Augmenting Naive Bayes for Ranking. In: Proceedings of the 22nd International Conference on Machine Learning, ICML 2005, pp. 1020-1027.

  • L. Jiang*, H. Zhang, and J. Su. Learning k-Nearest Neighbor Naive Bayes for Ranking. In: Proceedings of the 1st International Conference on Advanced Data Mining and Applications, ADMA 2005, LNAI 3584, pp.175-185.

  • L. Jiang*, H. Zhang, Z. Cai, and J. Su. One Dependence Augmented Naive Bayes. In: Proceedings of the 1st International Conference on Advanced Data Mining and Applications, ADMA 2005, LNAI 3584, pp. 186-194. 

  • H. Zhang*, L. Jiang, and J. Su. Hidden Naive Bayes. In: Proceedings of the 20th National Conference on Artificial Intelligence, AAAI 2005, pp. 919-924.

  • L. Jiang*, H. Zhang, and J. Su. Instance Cloning Local Naive Bayes. In: Proceedings of the 18th Canadian Conference on Artificial Intelligence, CAI 2005, LNAI 3501, pp. 280-291.

  • L. Jiang*, H. Zhang, Z. Cai, and J. Su. Learning Tree Augmented Naive Bayes for Ranking. In: Proceedings of the 10th International Conference on Database Systems for Advanced Applications, DASFAA 2005, LNCS 3453, pp. 688-698. 

  • L. Jiang*, H. Zhang, Z. Cai, and J. Su. Evolutional Naive Bayes. In: Proceedings of the 1st International Symposium on Intelligence Computation and Applications, ISICA 2005, pp. 344-350.

Activity:

Associate Editor, Journal of Intelligent & Fuzzy Systems, 2023-

Guest Editor, Special Issue in Mathematics: Class-Imbalance and Cost-Sensitive Learning, 2022

Guest Editor, Special Issue in Mathematics: Machine Learning and Data Mining: Techniques and Tasks, 2021

EB Member, Mathematics2021-

EB Member, Scientific Programming, 2021-

EB Member, Journal of Intelligent Systems, 2020-

EB Member, Oriental Journal of Computer Science and Technology, 2018-2020

EB Member, Open Computer Science, 2015-2020

EB Member, Central European Journal of Computer Science, 2011-2014

EB Member, Advances in Information Sciences and Service Sciences, 2010-2014

PC Member, 2023 International Conference on Machine Learning

PC Member, 2023 International Joint Conference on Artificial Intelligence

SPC Member, 2023 AAAI Conference on Artificial Intelligence

PC Member, 2023 International Conference on Uncertainty in Artificial Intelligence

PC Member, 2022 International Conference on Machine Learning

PC Member, 2022 International Joint Conference on Artificial Intelligence

PC Member, 2022 AAAI Conference on Artificial Intelligence

PC Member, 2022 Annual Conference on Neural Information Processing Systems

PC Member, 2022 International Conference on Uncertainty in Artificial Intelligence

PC Member, 2022 Pacific-Asia Conference on Knowledge Discovery and Data Mining

PC Member, 2022 International Conference on Learning Representations

PC Member, 2022 China Conference on Data Mining

PC Member, 2022 China Conference on Granular Computing and Knowledge Discovery

PC Member, 2021 International Conference on Machine Learning

SPC Member, 2021 International Joint Conference on Artificial Intelligence

PC Member, 2021 AAAI Conference on Artificial Intelligence

PC Member, 2021 Annual Conference on Neural Information Processing Systems

PC Member, 2021 International Conference on Uncertainty in Artificial Intelligence

PC Member, 2021 China Conference on Machine Learning

PC Member, 2021 CCF Conference on Artificial Intelligence

PC Member, 2021 IEEE International Conference on Big Knowledge

PC Member, 2020 International Joint Conference on Artificial Intelligence

PC Member, 2020 AAAI Conference on Artificial Intelligence

PC Member, 2020 China Conference on Data Mining

SPC Member, 2019 International Joint Conference on Artificial Intelligence

PC Member, 2019 AAAI Conference on Artificial Intelligence

PC Member, 2019 Pacific Rim International Conference on Artificial Intelligence

PC Member, 2018 AAAI Conference on Artificial Intelligence

PC Member, 2018 Pacific Rim International Conference on Artificial Intelligence

PC Member, 2018 International Conference on Computer Sciences and Applications

PC Member, 2018 International Conference on Data Mining and Applications

PC Member, 2017 International Joint Conference on Artificial Intelligence

PC Member, 2017 International Conference on Information Technology and Applications

PC Member, 2017 International Conference on Computer Sciences and Applications

PC Member, 2017 International Conference on Artificial Intelligence and Applications

PC Member, 2016 International Conference on Electronic and Information Technology

PC Member, 2016 International Conference on Computer Sciences and Applications

PC Member, 2015 International Conference on Computer Sciences and Applications

PC Member, 2014 International Conference on Artificial Neural Networks

Link:

CTeX

Overleaf

Eclipse Packages

UCI Machine Learning Repository

Journal and Conference Rankings by China Computer Federation

WEKA: Waikato Environment for Knowledge Analysis

CEKA: Crowd Environment and its Knowledge Analysis

KEEL: Knowledge Extraction based on Evolutionary Learning