2019吉林大学计算机科学与技术学院导师简介：王利民

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　　姓名：王利民

　　性别：男

　　职称：教授

　　最高学历：研究生

　　最高学位：博士

　　详细情况

　　所在学科专业：计算机软件与理论

　　所研究方向：机器学习，大数据挖掘，贝叶斯网络，概率逻辑推理

　　讲授课程：数据库原理

　　面向对象数据库

　　数据库与数据库安全(课程链接)

　　工作经历：2005年-2008年，吉林大学，计算机科学与技术学院，教师

　　2009年-至今，吉林大学，计算机科学与技术学院，数据库与智能网络研究室主任

　　科研项目：(1)吉林省自然科学基金“面向海量数据深度挖掘的无约束贝叶斯网络分类模型研究(高性能计算)”(No. 20150101014JC)，2015.1-2017.12。

　　(2)国家自然科学基金“面向关系数据库知识发现的概率逻辑贝叶斯网络研究”(No. 61272209)，2013.1-2016.12。

　　(3)教育部博士后基金项目“基于条件事件代数的贝叶斯网络逻辑表达及拓扑结构实现”(No. 2013M530980)，2013.1-2014.12。

　　(4)教育部博士后基金项目“面向智能汽车故障诊断的无约束贝叶斯网络研究”(No. 20100481053)，2011.1-2012.12。

　　(5)国家自然科学基金项目“面向智能信息处理的贝叶斯网络关键理论与方法”(No. 60275026)，2003.1-2005.12。

　　(6)国家科技支撑计划项目“省级应急平台和城市应急联动技术研发与示范(吉林省)”(No. 2006BAK01A33)，2006.11-2008.12。

　　(6)教育部高校博士点基金项目“面向多层次知识表达的贝叶斯分类模型研究”(No. 200801831011)，2009.1-2010.12。

　　学术论文：[1] LiMin Wang.General and Local: Averaged k-Dependence Bayesian Classifiers. Entropy, 2015, 17, 4134-4154.(SCI)

　　[2] LiMin Wang.Learning a Flexible K-Dependence Bayesian Classifier from the Chain Rule of Joint Probability Distribution. Entropy, 2015,17, 3766-3786. (SCI)

　　[3] LiMin Wang. Mining causal relationships among clinical variables for cancer diagnosis based on Bayesian analysis. BioData Mining, 2015, 8(13),1-15. (SCI)

　　[4] LiMin Wang,Minghui Sun. How to Mine Information from Each Instance to Extract an Abbreviated and Credible Logical Rule. Entropy, 2014, 16, 5242-5262.(SCI)

　　[5]LiMin Wang,ShuangChengWang. Extracting Credible Dependencies for Averaged One-Dependence Estimator Analysis. Mathematical Problems in Engineering, 2014, 14, 1-15.(SCI)

　　[6]LiangDong Hu, LiMin Wang. Using consensus bayesian network to model the reactive oxygen species regulatory pathway. PLOS ONE, 2013, 8(2),1-9.(SCI)

　　[7]LiMin Wang. Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference. Mathematical Problems in Engineering, 2013, 13,1-11.(SCI)

　　[8] LiMin Wang, GuoFeng Yao. Learning NT Bayesian Classifier Based on Canonical Cover Analysis of Relational Database. Information: An International Interdisciplinary Journal, 2012, 15(1), 165-172. (SCI，CT&IT2011推荐优秀论文)

　　[9] LiMin Wang, GuoFeng Yao. Extracting Logical Rules and Attribute Subset from Confidence Domain. Information: An International Interdisciplinary Journal, 2012, 15(1), 173-180. (SCI，CT&IT2011推荐优秀论文)

　　[10] LiMin Wang. Bayesian Network Inference Based on Functional Dependency Mining of Relational Database. Information: An International Interdisciplinary Journal. 2012, 15(6), 24411-2446. (SCI)

　　[11] LiMin Wang. Implementation of a scalable decision forest model based on information theory. Expert Systems with Applications, 2011, 38(5): 5981-5985. (SCI)

　　[12] LiMin Wang, XueBai Zang. Semi-Supervised Learning Based on Information Theory and Functional Dependency Rules of Probability. Advanced Science Letters, 2011, 4(2): 463-468. (SCI)

　　[13]LiangDong Hu, LiMin Wang, LiYan Dong. Quantitative Combination of Different Bayesian Networks. Procedia Engineering. 2011, 15(12), 3526–3530. (EI)

　　[14]王利民. 基于半监督学习的启发式值约简. 控制与决策, 2010, 25(10): 1531-1535. (EI)

　　[15]LiMin Wang. Towards Efficient Dimensionality Reduction for Evolving Bayesian Network Classifier. Advanced Materials Research, 2010, 108-111: 240-243. (EI)

　　[16]LiMin Wang. An Adaptive Ensemble Approach for Multi-level Semantic Knowledge Representation. Journal of Information & Computational Science, 2010, 7(1): 9-15. (EI)

　　[17]LiMin Wang. Class Dependent Feature Scaling Method via Restrictive Bayesian Network Classifier Combination. Journal of Computational Information Systems, 2010, 6(1): 33-38. (EI)

　　[18]王利民, 臧雪柏, 曹春红. 基于广义信息论的决策森林数据挖掘模型. 吉林大学学报(工学版), 2010, 40(1): 155-158. (EI)

　　[19]王利民. 基于广义信息论的贝叶斯分类器动态建模. 吉林大学学报(工学版), 39(3): 776-780, 2009. (EI)

　　[20] Wang LiMin, Xu PeiJuan, Li XiongFei. Learning Hybrid Bayesian Network Based on Divide and Conquer Strategy. Journal of Computational Information Systems, 3(2): 583-590, 2007. (EI )

　　[21]Wang LiMin, Cao ChunHong, Li XiongFei, Li HaiJun. Inference and Learning in Hybrid Probabilistic Network. Frontier of Computer Science in China, 1(4): 429-435, 2007. (EI )

　　[22]Wang LiMin, Zhang Zhijun, Cao ChunHong, Dong LiYan. Dimensionality reduction for evolving neural network. Journal of Computational Information Systems. 2(3): 1079-1084, 2006. (EI )

　　[23]Wang LiMin. Learning Bayesian-Neural Network from Mixed-mode Data. In Proceedings of the 13th International Conference on Neural Information Processing, 680-687, 2006. (SCI)

　　[24]Cao ChunHong, Zhang Bin, Wang LiMin. The Parametric Design Based on Organizational Evolutionary Algorithm. In Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, 940-944, 2006. (SCI)

　　[25]Wang LiMin, Cao ChunHong, Li HaiJun. Orthogonally Rotational Transformation for Naive Bayes Learning. In Proceedings of the 2005 International Conference on Computational Intelligence and Security, 145-150, 2005. (SCI)

　　[26]Wang LiMin, Cao ChunHong, Dong LiYan, Li XiaoLin. Generalized Tree Augmented Naive Bayes. Journal of Computational Information Systems, 1(4): 741-747, 2005. (EI)

　　[27]Wang LiMin, Li XiaoLin, Cao ChunHong, Yuan SenMiao. Combining Decision Tree and Naive Bayes for Classification. Knowledge-Based Systems, 10: 511-515, 2005. (SCI)

　　[28]Wang LiMin, Yuan SenMiao. Induction of hybrid decision tree based on post discretization strategy. Progress in Natural Science, 16: 541-545, 2004. (SCI)

　　[29]Wang LiMin, Yuan SenMiao, Li HaiJun, LiLing. Improving the Performance of Naive Bayes：A Hybrid Approach. In Proceedings of the 23th International Conference on Conceptual Modeling, 327-335, 2004. (SCI)

　　[30]Shenglei Chen, Ana M. Martínez, Geoffrey I. Webb, Limin Wang. Selective AnDE for large data learning: a low-bias memory constrained approach. Knowledge and Information Systems, 3: 1-29, 2016. (SCI)

　　[31]Shuangcheng Wang, Rui Gao, LiMin Wang. Bayesian network classifiers based on Gaussian kernel density. Expert Systems with Applications, 51:207-217, 2016. (SCI)

　　[32]Shenglei Chen, Ana M. Martínez, Geoffrey I. Webb, Limin Wang. Sample Based Attribute Selective AnDE for Large Data IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. 2017, 29(1): 172-185. (SCI)

　　[33]Li Min Wang, Fang Yuan Cao. Using k-dependence causal forest to mine the most significant dependency relationships

　　among clinical variables for thyroid disease diagnosis. PLOS ONE, 2017, 8, 1-17.

　　获奖情况：王利民等。贝叶斯网络概率逻辑表达及拓扑结构实现，2013年吉林省自然科学学术成果二等奖。

　　社会兼职：中国计算机学会(CCF)高级会员;中国人工智能学会不确定性人工智能专业委员会委员