李超群 (副教授)

副教授 硕士生导师

性别:女

学位:博士

所在单位:数学与物理学院

Email:

   

个人简历

李超群,中国地质大学(武汉)数学与物理学院副教授,硕士生导师,国际著名期刊EAAI编委,主要从事数据挖掘与机器学习领域的教学和研究工作。主持国家自然科学基金青年科学基金项目、中国高校产学研创新基金重点项目、湖北省自然科学基金面上项目等。迄今,已在TKDE、TSC、TNNLS、TSMCS、SCIS、PR、INS、FCS、KAIS、IJAR、IJISEAAIESWAKBS、NCA、APIN、PRL、JETAI、IJPRAI、INFFUS、ASOC、IJMLC、UAI、ICTAI、PRICAI等国际著名学术期刊和会议上发表学术论文60余篇,主编教材2部,副主编学术专著1部,授权国家发明专利7项,获批计算机软件著作权3项,荣获2021年度湖北省自然科学奖三等奖1项,指导学生获全国大学生数学建模竞赛国家一等奖3项、二等奖4项

工作经历:

2014-12至今,中国地质大学(武汉),数学与物理学院,副教授。

2009-122014-11,中国地质大学(武汉),数学与物理学院,讲师。

2005-07至2009-11,中国地质大学(武汉),数学与物理学院,助教

教育经历:

2015-03至2016-03,美国中阿肯色大学,计算机科学与技术,访问学者。

2009-09至2012.06,中国地质大学(武汉),地球探测与信息技术,博士。

2002-09至2005-06,华中科技大学,计算数学,硕士。

1998-09至2002-06,中国地质大学(武汉),数学,学士

主讲课程:

研究生课程:多元统计分析数据挖掘原理及应用。

本科生课程:多元统计分析;概率统计与随机过程数据挖掘算法;高等数学;线性代数;计算方法。

研究方向:

自2005年开始从事数据挖掘与机器学习(Data Mining and Machine Learning)领域的科研工作,主要研究方向包括:个性化推荐(Personalized Recommendation)、众包学习(Crowdsourcing Learning)、距离度量学习(Distance Metric Learning)、分类回归建模(Classification and Regression Modeling)。

科研项目:

面向众包的标签噪声处理算法研究,教育部人工智能重点实验室开放基金项目(No.AI2022004,2022.12-2024.11,主持)。

基于深度学习的电路板外观缺陷检测方法研究,中国高校产学研创新基金重点项目(No.2020ITA05008,2021.9-2022.8,主持)。

面向地质文本分类的众包标签噪声处理算法研究,湖北省智能地学信息处理重点实验室开放基金项目(No. KLIGIP2019A03, 2020.11-2022.10,主持)。

名词性属性距离度量中若干重要问题研究,中国地质大学中央高校基本科研业务费专项资金摇篮计划项目(No.CUG130414,2013.1-2015.12,主持)。

基于概率的名词性属性距离度量研究,国家自然科学基金青年科学基金项目(No.61203287,2013.1-2015.12,主持)。

基于贝叶斯网络的距离度量研究,湖北省自然科学基金面上项目(No.2012FFB6401,2012.1-2013.12,主持)。

基于K-近邻的统计学习算法及其应用研究,中国地质大学中央高校基本科研业务费专项资金优秀青年基金项目(No.CUGL090248,2009.11-2012.10,主持)。

科研论文:

J. Li, L. Jiang*, C. Li, and W. Zhang. Label Consistency-based Worker Filtering for Crowdsourcing. In: Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence, UAI 2024, PMLR xxx: xxxx-xxxx.

Z. Chen, L. Jiang*, W. Zhang, and C. Li. Weighted Adversarial Learning from Crowds. IEEE Transactions on Services Computing, doi: 10.1109/TSC.2024.3404353.

W. Zhang, L. Jiang*, Z. Chen, and C. Li. FNNWV: Farthest-Nearest Neighbor-based Weighted Voting for Class-Imbalanced Crowdsourcing. Science China Information Sciences, doi: 10.1007/s11432-023-3854-7.

Y. Zhang, L. Jiang*, and C. Li*. Instance Redistribution-based Label Integration for Crowdsourcing. Information Sciences, 2024, 674: 120702.

H. Li, L. Jiang*, and C. Li. Certainty Weighted Voting-based Noise Correction for Crowdsourcing. Pattern Recognition, 2024, 150: 110325.

L. Ren, L. Jiang*, W. Zhang, and C. Li. Label Distribution Similarity-based Noise Correction for Crowdsourcing. Frontiers of Computer Science, 2024, 18(5): 185323.

Y. Yang, L. Jiang*, and C. Li. A Self-Training-based Label Noise Correction Algorithm for Crowdsourcing. Acta Automatica Sinica, 2023, 49(4): 830-844.

X. Wu, L. Jiang*, W. Zhang, and C. Li. Three-way Decision-based Noise Correction for Crowdsourcing. International Journal of Approximate Reasoning, 2023, 160: 108973.

H. Zhang, L. Jiang*, W. Zhang, and C. Li. Multi-view Attribute Weighted Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(7): 7291-7302.

W. Li, C. Li*, and L. Jiang*. Learning from Crowds with Robust Logistic Regression. Information Sciences, 2023, 639: 119010.

X. Li, C. Li*, and L. Jiang. A Multi-View-Based Noise Correction Algorithm for Crowdsourcing Learning. Information Fusion, 2023, 91: 529-541.

L. Ren, L. Jiang*, and C. Li*. Label Confidence-Based Noise Correction for Crowdsourcing. Engineering Applications of Artificial Intelligence, 2023, 117: 105624.

Y. Hu, L. Jiang*, and C. Li*. Instance Difficulty-Based Noise Correction for Crowdsourcing. Expert Systems with Applications, 2023, 212: 118794.

Y. Zhang, L. Jiang*, and C. Li. Attribute Augmentation-Based Label Integration for Crowdsourcing. Frontiers of Computer Science, 2023, 17(5): 175331.

W. Yang, C. Li*, and L. Jiang. Learning from Crowds with Robust Support Vector Machines. Science China Information Sciences, 2023, 66(3): 132103.

W. Yang, C. Li*, and L. Jiang. Learning from Crowds with Decision Trees. Knowledge and Information Systems, 2022, 64(8): 2123-2140.

B. Ma, C. Li*, and L. Jiang. A Novel Ground Truth Inference Algorithm Based on Instance Similarity for Crowdsourcing Learning. Applied Intelligence, 2022, 52(15): 17784-17796.

H. Zhang, L. Jiang*, and C. Li. Attribute Augmented and Weighted Naive Bayes. Science China Information Sciences, 2022, 65(12): 222101.

L. Jiang*, H. Zhang, F. Tao, and C. Li. Learning from Crowds with Multiple Noisy Label Distribution Propagation. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(11): 6558-6568.

Z. Chen, L. Jiang*, and C. Li. Label Distribution-Based Noise Correction for Multiclass Crowdsourcing. International Journal of Intelligent Systems, 2022, 37(9): 5752-5767.

Z. Chen, L. Jiang*, and C. Li*. Label Augmented and Weighted Majority Voting for Crowdsourcing. Information Sciences, 2022, 606: 397-409.

Y. Dong, L. Jiang*, and C. Li. Improving Data and Model Quality in Crowdsourcing Using Co-Training-Based Noise Correction. Information Sciences, 2022, 583: 174-188.

Y. Yang, L. Jiang*, C. Li, and H. Li. A Tri-training-Based Label Noise Correction Algorithm for Crowdsourcing. Acta Electronica Sinica, 2021, 49 (3): 424-434.

W. Yang, C. Li*. Improving Crowd Labeling Using Stackelberg Models. International Journal of Machine Learning and Cybernetics, 2021, 12:1825–1838.

H. Zhang, L. Jiang*, and C. Li*. CS-ResNet: Cost-Sensitive Residual Convolutional Neural Network for PCB Cosmetic Defect Detection. Expert Systems with Applications, 2021, 185: 115673.

H. Zhang, L. Jiang*, and C. Li. Collaboratively Weighted Naive Bayes. Knowledge and Information Systems, 2021, 63(12): 3159-3182. 

L. Chen, L. Jiang*, and C. Li*. Using Modified Term Frequency to Improve Term Weighting for Text Classification. Engineering Applications of Artificial Intelligence, 2021, 101: 104215.

L. Chen, L. Jiang*, and C. Li*. Modified DFS-Based Term Weighting Scheme for Text Classification. Expert Systems with Applications, 2021, 168: 114438.

L. Jiang*, G. Kong, and C. Li. Wrapper Framework for Test-Cost-Sensitive Feature Selection. IEEE Transactions on Systems Man Cybernetics: Systems, 2021, 51(3): 1747-1756.

W. Xu, L. Jiang*, and C. Li. Resampling-Based Noise Correction for Crowdsourcing. Journal of Experimental & Theoretical Artificial Intelligence, 2021, 33(6): 985-999.

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.

F. Tao, L. Jiang*, and C. Li. Differential Evolution-Based Weighted Soft Majority Voting for Crowdsourcing. Engineering Applications of Artificial Intelligence, 2021, 106: 104474.

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.

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.

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

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. 

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.

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.

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

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

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.

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.

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.

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.

L. Jiang*, S. Wang, C. Li, and L. Zhang. Structure Extended Multinomial Naive Bayes. Information Sciences, 2016, 329: 346-356.

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.

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

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

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.

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

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

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

C. Li and H. Li*. Bayesian Network Classifiers for Probability-Based Metrics. Journal of Experimental & Theoretical Artificial Intelligence, 2013, 25(4): 477-491.

C. Li and H. Li*. Selective Value Difference Metric. Journal of Computers, 2013, 8(9): 2232-2238

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.

C. Li and H. Li*. A Modified Short and Fukunaga Metric Based  on  the Attribute Independence Assumption. Pattern Recognition Letters, 2012, 33(9): 1213-1218.

C. Li and H. Li*. One Dependence Value Difference Metric. Knowledge-Based Systems, 2011, 24(5): 589-594.

C. Li and H. Li*. Correlation Weighted Heterogeneous Euclidean-Overlap Metric. International Journal of Computers and Applications, 2011, 33(4): 341-346.

C. Li and H. Li*. Learning Random Model Trees for Regression. International Journal of Computers and Applications, 2011, 33(3): 258-265.

C. Li and H. Li*. A Survey of Distance Metrics for Nominal Attributes. Journal of Software, 2010, 5(11): 1262-1269.

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

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.

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.

成果奖励:

2021年度湖北省自然科学奖三等奖(贝叶斯分类:模型、算法与应用,序2)。

教材专著:

工科数学分析练习与提高(一), 中国地质大学出版社,2018年,主编。

工科数学分析练习与提高(二), 中国地质大学出版社,2018年,主编。

贝叶斯网络分类器:算法与应用,中国地质大学出版社,2015年,副主编,序2。

发明专利:

蒋良孝、张文均、张欢、李超群,一种基于随机森林的伯努利朴素贝叶斯文本分类方法,专利号:ZL202010125450.9,授权公告日:2023-4-7。

蒋良孝、陈龙、李超群,一种基于特异性的词频加权方法及文本分类方法,专利号:ZL202010097291.6,授权公告日:2023-4-7。

蒋良孝,邵诗琪,陈龙,李超群。一种隐多项式朴素贝叶斯文本分类方法及装置,专利号:ZL201910338569.1,授权公告日:2022-11-18。

蒋良孝,王沙沙,李超群。一种基于文档长度的实例加权方法及文本分类方法,专利号:ZL201510395998.4,授权公告日:2018-10-19。

蒋良孝,张伦干,李超群。一种基于决策树的属性加权方法及文本分类方法,专利号:ZL201510237748.8,授权公告日:2018-5-22。

蒋良孝,王沙沙,李超群,张伦干。一种结构扩展的多项式朴素贝叶斯文本分类方法,专利号:ZL201510366258.8,授权公告日:2018-5-1。

蒋良孝,张伦干,李超群。一种基于信息增益率的属性选择方法,专利号:ZL201510173354.0,授权公告日:2017-11-21。

软件著作:

蒋良孝,李超群。距离度量学习软件,软件登记号:2018SR112546。

蒋良孝,李超群,卢航航。油水层识别软件,软件登记号:2017SR178464。

蒋良孝,卢航航,李超群。储层孔隙度预测软件,软件登记号:2017SR169525。

学术指导:

指导本科生获2023年全国大学生数学建模竞赛国家二等奖1项(吕昕桦,陈子阳,雷梦奇),湖北省一等奖1项、二等奖1项、三等奖2项。

指导本科生获2022年全国大学生数学建模竞赛国家二等奖1项(蔄宇飞,胡楠,刘志鑫),湖北省一等奖2项、二等奖2项、三等奖2项。

指导本科生获2021年全国大学生数学建模竞赛国家一等奖1项(叶诗洋,李雯玥,苏海瑞),湖北省一等奖2项、三等奖2项。

指导本科生获2020年全国大学生数学建模竞赛国家二等奖2项(王芊芊,张洁飞,苏春银;黄思睿,衷雨欣,潘洁),湖北省二等奖1项、三等奖1项。

指导本科生获2019年全国大学生数学建模竞赛湖北省一等奖1项、二等奖2项、三等奖2项。

指导本科生获2018年全国大学生数学建模竞赛国家一等奖1项(邓昊,王风栋,翟明键)。

指导本科生获2016年全国大学生数学建模竞赛国家一等奖1项(殷欣,杨晓伟,马莉珍)。

指导本科生获2021年国家级大学生创新训练计划项目1项(王健等)。 

指导本科生获2020年国家级大学生创新训练计划项目1项(梁庭辉等)。

指导本科生获2018年国家级大学生创新训练计划项目1项(史伟等)。

指导本科生获2013年湖北省优秀学士学位论文(朱民峰)。

指导本科生2020级李四光计划(胡志博)。

指导本科生2019级李四光计划(王健)。

指导本科生获2018级李四光计划(梁庭辉)。

人才培养:

2023级:黄思健;宋姿颖;田谨铭;叶梦甜。

2022级:艾语嫣;金瑾;彭榛;曾梦琦。

2021级:沈澳奇(2021、2022年度中国研究生数学建模竞赛三等奖);杨慧慧(2021、2022年度中国研究生数学建模竞赛三等奖);阳华。

2020级:贺明贵;李欣阳;李文斌(2023年度中国地质大学优秀硕士学位论文);孙传佳。

2019级:马奔;王银(2020年度中国研究生数学建模竞赛三等奖)。

2018级:杨文军(2020年度硕士研究生国家奖学金;2019年度中国研究生数学建模竞赛一等奖)。