2019吉林大学计算机科学与技术学院导师简介：王利民
对考生而言，充分了解高校、专业以及师资情况是一项最基础、最关键的工作。以下是新东方在线为大家整理的“吉林大学计算机科学与技术学院导师简介：王利民”的相关信息，希望对同学们有所帮助。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： LiMin Wang.General and Local: Averaged k-Dependence Bayesian
Classifiers. Entropy, 2015, 17, 4134-4154.(SCI)
LiMin Wang.Learning a Flexible K-Dependence Bayesian Classifier from
the Chain Rule of Joint Probability Distribution. Entropy, 2015,17, 3766-3786.
(SCI)
LiMin Wang. Mining causal relationships among clinical variables for
cancer diagnosis based on Bayesian analysis. BioData Mining, 2015, 8(13),1-15.
(SCI)
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)
LiMin Wang,ShuangChengWang. Extracting Credible Dependencies for
Averaged One-Dependence Estimator Analysis. Mathematical Problems in
Engineering, 2014, 14, 1-15.(SCI)
LiangDong Hu, LiMin Wang. Using consensus bayesian network to model the
reactive oxygen species regulatory pathway. PLOS ONE, 2013, 8(2),1-9.(SCI)
LiMin Wang. Extraction of Belief Knowledge from a Relational Database
for Quantitative Bayesian Network Inference. Mathematical Problems in
Engineering, 2013, 13,1-11.(SCI)
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)
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)
LiMin Wang. Bayesian Network Inference Based on Functional Dependency
Mining of Relational Database. Information: An International Interdisciplinary
Journal. 2012, 15(6), 24411-2446. (SCI)
LiMin Wang. Implementation of a scalable decision forest model based
on information theory. Expert Systems with Applications, 2011, 38(5): 5981-5985.
(SCI)
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)
LiangDong Hu, LiMin Wang, LiYan Dong. Quantitative Combination of
Different Bayesian Networks. Procedia Engineering. 2011, 15(12), 3526–3530.
(EI)
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)
LiMin Wang. Towards Efficient Dimensionality Reduction for Evolving
Bayesian Network Classifier. Advanced Materials Research, 2010, 108-111:
240-243. (EI)
LiMin Wang. An Adaptive Ensemble Approach for Multi-level Semantic
Knowledge Representation. Journal of Information & Computational Science,
2010, 7(1): 9-15. (EI)
LiMin Wang. Class Dependent Feature Scaling Method via Restrictive
Bayesian Network Classifier Combination. Journal of Computational Information
Systems, 2010, 6(1): 33-38. (EI)
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)
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)
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 )
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 )
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 )
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)
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)
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)
Wang LiMin, Cao ChunHong, Dong LiYan, Li XiaoLin. Generalized Tree
Augmented Naive Bayes. Journal of Computational Information Systems, 1(4):
741-747, 2005. (EI)
Wang LiMin, Li XiaoLin, Cao ChunHong, Yuan SenMiao. Combining Decision
Tree and Naive Bayes for Classification. Knowledge-Based Systems, 10: 511-515,
2005. (SCI)
Wang LiMin, Yuan SenMiao. Induction of hybrid decision tree based on
post discretization strategy. Progress in Natural Science, 16: 541-545, 2004.
(SCI)
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)
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)
Shuangcheng Wang, Rui Gao, LiMin Wang. Bayesian network classifiers
based on Gaussian kernel density. Expert Systems with Applications, 51:207-217,
2016. (SCI)
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)
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
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