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李超群

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Gender : Female
Degree : Doctoral Degree
School/Department : 数学与物理学院
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Personal Profile

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,主要从事数据挖掘与机器学习方向的教学和研究工作。主持并完成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IEEE TKDE、IEEE TNNLS、IEEE TSMC-S、SCIS、FCS、INS、INFFUS、KAIS、IJIS、KBS、ESWA、EAAI、NCAAPINPRLJETAI、IJPRAI、IJMLC、IJCA等国际重要学术期刊上发表学术论文50余篇,副主编学术专著1部,授权国家发明专利5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(序2)

教育经历:

2009-092012-06中国地质大学(武汉),地球探测与信息技术,博士。

2002-092005-06,华中科技大学,计算数学,硕士。

1998-092002-06中国地质大学(武汉),数学,学士。

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。

2005-072009-11,中国地质大学(武汉),数学与物理学院,助教。

主讲课程:

研究生课程:数据挖掘原理及应用;多元统计分析。

本科生课程:数据挖掘算法;多元统计分析;高等数学;概率论与数理统计;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)方向的科研工作,主要研究领域包括:距离度量学习(Distance Metric Learning)、分类回归建模(Classification and Regression Modeling)、众包学习(Crowdsourcing Learning)、个性化推荐(Personalized Recommendation)

科研项目:

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,智能地学信息处理湖北省重点实验室开放基金项目(No. KLIGIP-2019A03, 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.CUGL090248,2009.11-2012.12,主持)。

发明专利:

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

科研论文:

H. Zhang, L. Jiang*, W. Zhang, and C. Li. Multi-view Attribute Weighted Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2022, doi: 10.1109/TKDE.2022.3177634.

Y. Zhang, L. Jiang*, and C. Li. Attribute Augmentation-based Label Integration for Crowdsourcing. Frontiers of Computer Science, 2022, doi: 10.1007/s11704-022-2225-z.

W. Yang, C. Li*, and L. Jiang. Learning from Crowds with Robust Support Vector Machines. Science China Information Sciences, 2020, doi: 10.1007/s11432-020-3067-8.

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.

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

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.

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

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.

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.

科研获奖:

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)。

专著,教材,教辅:

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

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。

学术指导:

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)。

指导本科生入选2020级李四光计划(胡志博)。

指导本科生入选2019级李四光计划(王健)。

指导本科生入选2018级李四光计划(梁庭辉)。

指导本科生获2013年湖北省优秀学士学位论文(朱民峰)。

人才培养:

2022级:艾语嫣;金瑾;彭榛;曾梦琦。

2021级:沈澳奇(2021年度全国研究生数学建模竞赛国家三等奖);杨慧慧(2021年度全国研究生数学建模竞赛国家三等奖);阳华。

2020级:贺明贵;李欣阳;李文斌;孙传佳。

2019级:马奔;王银(2020年度全国研究生数学建模竞赛国家三等奖)。

2018级:杨文军(2020年度硕士研究生国家奖学金;2019年度全国研究生数学建模竞赛国家一等奖)。