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李超群

发布人:发表时间:2017-05-09点击:

最新情况请见地大个人主页:http://grzy.cug.edu.cn/lcq/

个人简介

李超群,新萄京集团3522cc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,湖北省自然科学基金面上项目,中国地质大学摇篮计划等项目。迄今,已在IEEE TKDEIEEE TNNLSIEEE TSMC-SSCISINSKAISKBSESWAEAAIIJISJETAIPRLAPINFCSIJPRAIIJMLC等国际重要学术期刊上发表学术论文40余篇,副主编学术专著1部,授权国家发明专利4项,获批计算机软件著作权2项。

教育经历:

20099-20126月,中国地质大学地空学院,博士。

20029-20056月,华中科技大学数学系,硕士。

19989-20026月,中国地质大学新萄京集团3522cc,本科。

工作经历:

20153-20163月,美国中阿肯色大学,访问学者。

201412-至今,中国地质大学新萄京集团3522cc,副教授。

200912-201411月,中国地质大学新萄京集团3522cc,讲师。

20057-200911月,中国地质大学新萄京集团3522cc,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:

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-新一代信息技术创新项目(No.2020ITA050082021.9-2022.8,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课题(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(No.CUG1304142013.1-2015.12,主持)。

基于概率的名词性属性距离度量研究,国家青年科学基金项目(No.612032872013.1-2015.12,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.2012FFB64012012.1-2013.12,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(No.CUGL0902482009.11-2012.12,主持)。

发明专利:

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

科研论文:

W. Yang, C. Li*, and L. Jiang. Learning from Crowds with Decision Trees. Knowledge and Information Systems, 2022, Accepted for Publication.

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.

Z. Chen, L. Jiang*, and C. Li. Label Distribution-based Noise Correction for Multiclass Crowdsourcing. International Journal of Intelligent Systems, 2022, doi: 10.1002/int.22812.

B. Ma, C. Li*, and L. Jiang. A Novel Ground Truth Inference Algorithm Based on Instance Similarity for Crowdsourcing Learning. Applied Intelligence, 2022, doi: 10.1007/s10489-022-03433-3.

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, 2021, doi: 10.1109/TNNLS.2021.3082496.

H. Zhang, L. Jiang*, and C. Li. Attribute Augmented and Weighted Naive Bayes. Science China Information Sciences, 2021, doi: 10.1007/s11432-020-3277-0.

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.

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

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.

W. Yang, C. Li*. Improving crowd labeling using Stackelberg models. International Journal of Machine Learning and Cybernetics, 2021, 12:1825–1838.

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

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. 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. 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.

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.

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. 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.

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*, 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 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.

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

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。

指导学生:

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)省一等奖2项,省三等奖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),省二等奖1项,省三等奖1项。

指导本科生获2019年全国大学生数学建模竞赛省一等奖1项,省二等奖2项,省三等奖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)。

研究生培养:

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

2020级:贺明贵;李欣阳;李文斌;孙传佳。

2019级: 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年度国家硕士研究生奖学金;2019年度全国研究生数学建模竞赛国家一等奖)。

教师其他联系方式

· [1] 邮箱 : chqli@cug.edu.cn