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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 machine learning and data mining. In these areas he has published more than 80 papers in peer-reviewed international journals and conferences such as TKDE, TSMC, PR, INS, KAIS, SCIS, KBS, EAAI, ESWA, NCA, JETAI, PRL, JIIS, IJPRAI, FCS, ICML, IJCAI, AAAI, ICDM, DASFAA, ICTAI, ICANN, IJCNN, and PRICAI. Besides, he serves/ed as EB/PC Members of several international journals and conferences such as BMRI, JIS, JAIS, OJCST, OCS, CEJCS, AISS, IJCAI, AAAI, ICANN, and PRICAI.

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

Career:

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

2010/01-2010/06, Visiting Scholar at Concordia University

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

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

2005/08-2005/10, Visiting Scholar at University of New Brunswick

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

2004/03-2004/08, Visiting Scholar at University of New Brunswick

Interest:

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

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

Course:

Machine Learning (For Undergraduate Students)

Data Mining and Machine Learning (For Graduate Students)

Paper: (*Corresponding authors) 

  1. W. Yang, C. Li*, and L. Jiang. Learning from Crowds with Robust Support Vector Machines. SCIENCE CHINA Information Sciences, Online, DOI: 10.1007/s11432-020-3067-8. (CCF-B, CUG-T2)

  2. W. Xu, L. Jiang*, and C. Li. Resampling-based Noise Correction for Crowdsourcing. Journal of Experimental & Theoretical Artificial Intelligence, Online, DOI: 10.1080/0952813X.2020.1806519.

  3. L. Jiang*, G. Kong, and C. Li. Wrapper Framework for Test-Cost-Sensitive Feature Selection. IEEE Transactions on Systems Man Cybernetics-Systems, Online, DOI: 10.1109/TSMC.2019.2904662. (CUG-T2)

  4. L. Chen, L. Jiang*, and C. Li*. Modified DFS-based term weighting scheme for text classification. Expert Systems with Applications, 2021, 168: 114438. (CUG-T2)

  5. H. Zhang, L. Jiang*, and L. Yu. Attribute and Instance Weighted Naive Bayes. Pattern Recognition, 2021, 111: 107674. (CCF-B, CUG-T2)

  6. W. Xu, L. Jiang*, and C. Li. Improving Data and Model Quality in Crowdsourcing Using Cross-Entropy-based Noise Correction. Information Sciences, 2021, 546: 803-814. (CCF-B, CUG-T1)

  7. F. Gong, L. Jiang*, H. Zhang, D. Wang, and X. Guo. Gain Ratio Weighted Inverted Specific-Class Distance Measure for Nominal Attributes. International Journal of Machine Learning and Cybernetics, 2020, 11(10): 2237-2246.

  8. F. Gong, L. Jiang*, D. Wang, and X. Guo. Averaged One-Dependence Inverted Specific-Class Distance Measure for Nominal Attributes. Journal of Experimental & Theoretical Artificial Intelligence, 2020, 32(4): 651-663.

  9. F. Tao, L. Jiang* and C. Li. Label Similarity-based Weighted Soft Majority Voting and Pairing for Crowdsourcing. Knowledge and Information Systems, 2020, 62(7): 2521-2538. (CCF-B, CUG-T2)

  10. H. Zhang, L. Jiang*, and L. Yu. Class-specific Attribute Value Weighting for Naive Bayes. Information Sciences, 2020, 508: 260-274. (CCF-B, CUG-T1)

  11. L. Yu, L. Jiang*, D. Wang, and L. Zhang. Toward Naive Bayes with Attribute Value Weighting. Neural Computing & Applications, 2019, 31(10): 5699-5713.

  12. L. Zhang, L. Jiang*, and C. Li. A Discriminative Model Selection Approach and Its Application to Text Classification. Neural Computing & Applications, 2019, 31(4): 1173-1187.

  13. C. Li*, L. Jiang, and W. Xu. Noise Correction to Improve Data and Model Quality for Crowdsourcing. Engineering Applications of Artificial Intelligence, 2019, 82: 184-191. (CUG-T2)

  14. W. Xu, L. Jiang*, and L. Yu. An Attribute Value Frequency-based Instance Weighting Filter for Naive Bayes. Journal of Experimental & Theoretical Artificial Intelligence, 2019, 31(2): 225-236.

  15. L. Jiang* and C. Li. Two Improved Attribute Weighting Schemes for Value Difference Metric. Knowledge and Information Systems, 2019, 60(2): 949-970. (CCF-B, CUG-T2)

  16. L. Jiang*, L. Zhang, L. Yu, and D. Wang. Class-specific Attribute Weighted Naive Bayes. Pattern Recognition, 2019, 88: 321-330. (CCF-B, CUG-T2)

  17. L. Jiang*, L. Zhang, C. Li, and J. Wu. A Correlation-based Feature Weighting Filter for Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 201-213.  (CCF-A, CUG-T2, ESI高被引论文)

  18. 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. (CCF-A, CUG-T2)

  19. 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.

  20. 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.

  21. 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.

  22. L. Yu, L. Jiang*, D. Wang, and L. Zhang. Attribute Value Weighted Average of One-dependence Estimators. Entropy, 2017, 19(9): 501.

  23. C. Li, L. Jiang*, H. Li, J. Wu, and P. Zhang. Toward Value Difference Metric with Attribute Weighting. Knowledge and Information Systems, 2017, 50(3): 795-825. (CCF-B, CUG-T2)

  24. C. Qiu, L. Jiang*, and C. Li. Randomly Selected Decision Tree for Test-Cost Sensitive Learning. Applied Soft Computing, 2017, 53: 27-33. (CUG-T2)

  25. G. Kong, L. Jiang*, and C. Li*. Beyond Accuracy: Learning Selective Bayesian Classifiers with Minimal Test Cost. Pattern Recognition Letters, 2016, 80: 165-171.

  26. C. Li, S. Sheng, L. Jiang*, and H. Li*. Noise Filtering to Improve Data and Model Quality for Crowdsourcing. Knowledge-Based Systems, 2016, 107: 96-103. (CUG-T2)

  27. L. Zhang, L. Jiang*, C. Li*, and G. Kong. Two Feature Weighting Approaches for Naive Bayes Text Classifiers. Knowledge-Based Systems, 2016, 100: 137-144. (CUG-T2)

  28. L. Zhang, L. Jiang*, and C. Li. A New Feature Selection Approach to Naive Bayes Text Classifiers. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(2): 1650003.

  29. L. Jiang*, C. Li*, S. Wang, and L. Zhang. Deep Feature Weighting for Naive Bayes and Its Application to Text Classification. Engineering Applications of Artificial Intelligence, 2016, 52: 26-39. (CUG-T2, ESI高被引论文)

  30. L. Jiang*, S. Wang, C. Li, and L. Zhang. Structure Extended Multinomial Naive Bayes. Information Sciences, 2016, 329: 346-356. (CCF-B, CUG-T1)

  31. 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. (Student Travel Award)

  32. C. Qiu, L. Jiang*, and C. Li. Not always simple classification: Learning SuperParent for Class Probability Estimation. Expert Systems with Applications, 2015, 42(13): 5433-5440. (CUG-T2)

  33. S. Wang, L. Jiang*, and C. Li. Adapting Naive Bayes Tree for Text Classification. Knowledge and Information Systems, 2015, 44(1): 77-89. (CCF-B, CUG-T2)

  34. L. Jiang*, C. Qiu, and C. Li. A Novel Minority Cloning Technique for Cost-Sensitive Learning. International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29(4): 1551004.

  35. 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.

  36. L. Jiang*, C. Li, and S. Wang. Cost-Sensitive Bayesian Network Classifiers. Pattern Recognition Letters, 2014, 45: 211-216.

  37. L. Jiang*, C. Li, H. Zhang, and Z. Cai. A Novel Distance Function: Frequency Difference Metric. International Journal of Pattern Recognition and Artificial Intelligence, 2014, 28(2): 1451002.

  38. L. Jiang*, Z. Cai, D. Wang, and H. Zhang. Bayesian Citation-KNN with Distance Weighting. International Journal of Machine Learning and Cybernetics, 2014, 5(2): 193-199.

  39. C. Li*, L. Jiang, and H. Li. Naive Bayes for Value Difference Metric. Frontiers of Computer Science, 2014, 8(2): 255-264.

  40. C. Li*, L. Jiang, and H. Li. Local Value Difference Metric. Pattern Recognition Letters, 2014, 49: 62-68.

  41. 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.

  42. G. Li, O. Bräysy, L. Jiang, Z. Wu*, and Y. Wang. Finding Time Series Discord Based on Bit Representation Clustering. Knowledge-Based Systems, 2013, 54: 243-254. (CUG-T2)

  43. L. Jiang* and C. Li. An Augmented Value Difference Measure. Pattern Recognition Letters, 2013, 34(10): 1169-1174.

  44. L. Jiang, Z. Cai*, H. Zhang, and D. Wang. Naive Bayes Text Classifiers: A Locally Weighted Learning Approach. Journal of Experimental & Theoretical Artificial Intelligence, 2013, 25(2): 273-286.

  45. 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.

  46. 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.

  47. L. Jiang*, Z. Cai*, H. Zhang, and D. Wang. Not so greedy: Randomly Selected Naive Bayes. Expert Systems with Applications, 2012, 39(12): 11022-11028. (CUG-T2)

  48. L. Jiang*, H. Zhang, Z. Cai, and D. Wang. Weighted Average of One-Dependence Estimators. Journal of Experimental & Theoretical Artificial Intelligence, 2012, 24(2): 219-230. [source code in WEKA 3.7.1]

  49. L. Jiang*, D. Wang, and Z. Cai. Discriminatively Weighted Naive Bayes and Its Application in Text Classification. International Journal on Artificial Intelligence Tools, 2012, 21(1): 1250007.

  50. L. Jiang, Z. Cai*, D. Wang, and H. Zhang. Improving Tree Augmented Naive Bayes for Class Probability Estimation. Knowledge-Based Systems, 2012, 26: 239-245. (CUG-T2)

  51. L. Jiang*. Learning Instance Weighted Naive Bayes from Labeled and Unlabeled Data. Journal of Intelligent Information Systems, 2012, 38(1): 257-268.

  52. L. Jiang* and C. Li. Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination. Journal of Computers, 2011, 6(7): 1325-1331.

  53. L. Jiang* and C. Li. An Empirical Study on Class Probability Estimates in Decision Tree Learning. Journal of Software, 2011, 6(7): 1368-1373.

  54. L. Jiang*. Learning Random Forests for Ranking. Frontiers of Computer Science in China, 2011, 5(1): 79-86.

  55. L. Jiang*. Random One-Dependence Estimators. Pattern Recognition Letters, 2011, 32(3): 532-539.

  56. L. Jiang*, Z. Cai, and D. Wang. Improving Naive Bayes for Classification. International Journal of Computers and Applications, 2010, 32(3): 328-332.

  57. L. Jiang and C. Li*. An Empirical Study on Attribute Selection Measures in Decision Tree Learning. Journal of Computational Information Systems, 2010, 6(1): 105-112.

  58. L. Jiang*, H. Zhang, and Z. Cai. A Novel Bayes Model: Hidden Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(10): 1361-1371. (CCF-A, CUG-T2)

  59. L. Jiang*, C. Li, and Z. Cai. Learning Decision Tree for Ranking. Knowledge and Information Systems, 2009, 20(1): 123-135. (CCF-B, CUG-T2)

  60. L. Jiang*, C. Li, and Z. Cai. Decision Tree with Better Class Probability Estimation. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23(4): 745-763.

  61. L. Jiang*, D. Wang, Z. Cai, S. Jiang, and X. Yan. Scaling Up the Accuracy of K-Nearest-Neighbor Classifiers: A Naive-Bayes Hybrid. International Journal of Computers and Applications, 2009, 31(1): 36-43.

  62. L. Jiang*, Z. Cai, and D. Wang. Learning Averaged One-Dependence Estimators by Instance Weighting. Journal of Computational Information Systems, 2008, 4(6): 2753-2760.

  63. L. Jiang*, D. Wang, H. Zhang, Z. Cai, and B. Huang. Using Instance Cloning to Improve Naive Bayes for Ranking. International Journal of Pattern Recognition and Artificial Intelligence, 2008, 22(6): 1121-1140.

  64. W. Gong*, Z. Cai, and L. Jiang. Enhancing the Performance of Differential Evolution Using Orthogonal Design Method. Applied Mathematics and Computation, 2008, 206(1): 56-69. (CUG-T2)

  65. 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.

  66. 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.

  67. 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.

  68. 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.

  69. 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.

  70. 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.

  71. 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.

  72. 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.

  73. 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. [source code in WEKA 3.7.1]

  74. 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.

  75. 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.

  76. 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.

  77. 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. (CCF-B)

  78. 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.

  79. 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. (CCF-A, CUG-T2)

  80. 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.

  81. 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.

  82. 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. (CCF-A, CUG-T2)

  83. 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.

  84. 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. (CCF-B)

  85. 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:

EB Member, BioMed Research International, 2020-

EB Member, Journal of Intelligent Systems, 2020-

EB Member, Journal of Artificial Intelligence and Systems, 2019-

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

EB Member, Open Computer Science, 2015-

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

EB Member, Advances in Information Sciences and Service Sciences, 2010-2018

SPC Member, 2021 International Joint Conference on Artificial Intelligence

PC Member, 2021 AAAI Conference on Artificial Intelligence

PC Member, 2021 China Conference on Machine Learning

PC Member, 2021 CCF Conference on Artificial Intelligence

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:

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UCI Machine Learning Repository

WEKA: Waikato Environment for Knowledge Analysis

MEKA: A Multi-label Extension to WEKA

CEKA: Crowd Environment and its Knowledge Analysis

KEEL: Knowledge Extraction based on Evolutionary Learning

Journal and Conference Rankings by China Computer Federation