王利民

人工智能，机器学习，贝叶斯网络，概率因果推理

面向对象数据库，数据库安全，数据库原理

工作经历：

2012年-2013年，澳大利亚莫纳什大学 研究员

科研项目：

(1) 吉林省自然科学基金“基于贝叶斯深度学习的大气污染源特征识别和大气污染动态演化研究”（No. 20240101371JC），2024.1-2026.12。

(2) 国家重点研发计划项目“场地土壤污染成因与治理技术”（No. 2019YFC1804804），2020.1-2022.12。

(3）吉林省自然科学基金“基于贝叶斯网络的图式知识表达和因果推理关键问题研究”（No. 20200201281JC），2020.1-2022.12。

(4）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。

(5）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。

(6）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。

(7）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。

学术论文：

在国内外权威学术期刊IEEE TBD、PR、IDA、IJB、KBS、ESWA、APIN、JETAI发表相关学术论文60余篇。部分发表的文章目录如下：

[1] Wang, L., Li, L., Li, Q. Learning high-dependence Bayesian network classifier with robust topology. Expert Systems with Applications, 2024，239: 122395. (中科院SCI一区)

[2] Wang, L., Wang, J., Guo, L. Efficient heuristics for learning scalable Bayesian network classifier from labeled and unlabeled data. Applied Intelligence, 2024，1-23. (中科院SCI二区)

[3] Wang, L., Wei, J., Li, K., Zhou, J. Exploiting the implicit independence assumption for learning directed graphical models. Intelligent Data Analysis, 2024. (SCI)

[4] Wang, L., Wang, L., Guo, L., Li, Q. Exploring complex multivariate probability distributions with simple and robust bayesian network topology for classification. Applied Intelligence, 2023, 53(24): 29799-29817. (中科院SCI二区)

[5] Kong, H., Wang, L. Flexible model weighting for one-dependence estimators based on point-wise independence analysis. Pattern Recognition, 2023, 139: 109473. (中科院SCI一区)

[6] Wang, L., Fan, H., Kong, H. From undirected dependence to directed causality: A novel Bayesian learning approach. Intelligent Data Analysis, 2022, 26(5): 1275-1302. (SCI)

[7] Wang, L., Zhou, J., Wei, J., Pang, M. Learning causal Bayesian networks based on causality analysis for classification. Engineering Applications of Artificial Intelligence, 2022, 114: 105212. (中科院SCI一区)

[8] Wang, L., Xie, Y., Pang, M. Alleviating the attribute conditional independence and IID assumptions of averaged one-dependence estimator by double weighting. Knowledge Based Systems, 2022, 250: 109078. (中科院SCI一区)

[9] Shenglei Chen, Xin Ma, Linyuan Liu and Limin Wang. Selective AnDE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI). Journal of Experimental & Theoretical Artificial Intelligence, 2022, 1-20. (中科院SCI二区)

[10] Yi Ren, Limin Wang, Xiongfei Li, Meng Pang and Junyang Wei. Stochastic optimization for bayesian network classifiers. Applied Intelligence, 2022. (中科院SCI二区)

[11] Limin Wang, Xinhao Zhang, Kuo Li and Shuai Zhang. Semi-supervised learning for k-dependence Bayesian classifiers. Applied Intelligence, 2022, 52, 3604-3622. (中科院SCI二区)

[12] Limin Wang, Shuai Zhang, Musa Mammadov, Kuo Li, Xinhao Zhang and Siyuan Wu. Semi-supervised weighting for averaged one-dependence estimators. Applied Intelligence, 2022, 52, 4057-4073. (中科院SCI二区)

[13] 刘洋，王利民，孙铭会. 基于信息熵函数的启发式贝叶斯因果推理. 计算机学报，2021, 44, 2135-2147. (CCF中文A类)

[14] He Kong, Xiaohu Shi, Limin Wang, Yang Liu, Musa Mammadov and Gaojie Wang. Averaged tree-augmented one-dependence estimators. Applied Intelligence, 2021, 51, 4270-4286. (中科院SCI二区)

[15] Limin Wang, Sikai Qi, Yang Liu, Hua Lou and Xin Zuo. Bagging k-dependence Bayesian network classifiers. Intelligent Data Analysis, 2021, 25 (3), 641-667. (SCI)

[16] Yang Liu, Limin Wang, Musa Mammadov, Shenglei Chen, Gaojie Wang, Sikai Qi and Minghui Sun. Hierarchical Independence Thresholding for learning Bayesian network classifiers. Knowledge-Based Systems, 2021, 212, 106627. (中科院SCI一区)

[17] Limin Wang, Peng Chen, Shenglei Chen and Minghui Sun. A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier. Intelligent Data Analysis, 2021, 25, 35-55. (SCI)

[18] Yang Liu, Limin Wang and Musa Mammadov. Learning semi-lazy Bayesian network classifier under the c.i.i.d assumption. Knowledge-Based Systems, 2020, 208, 106422. (中科院SCI一区)

[19] Hua Lou, Gaojie Wang, Limin Wang and Musa Mammadov. Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions. IEEE Access, 2020, 8, 150465-150477. (SCI)

[20] Zhiyi Duan, Limin Wang, Shenglei Chen and Minghui Sun. Instance-based weighting filter for superparent one-dependence estimators. Knowledge-Based Systems, 2020, 203, 106085. (中科院SCI一区)

[21] Zhiyi Duan, Limin Wang and Minghui Sun. Efficient heuristics for learning Bayesian network from labeled and unlabeled data. Intelligent Data Analysis, 2020, 24, 385-408. (SCI)

[22] Limin Wang, Jie Chen, Yang Liu and Minghui Sun. Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators. IEEE Access, 2020, 8, 27887-27900. (SCI)

[23] 王利民, 姜汉民. 强化属性依赖关系的K阶贝叶斯分类模型. 控制与决策, 2019, 34(6), 1234-1240. (EI)

[24] Siqi Gao, Hua Lou, Limin Wang, Yang Liu and Tiehu Fan. Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning. Entropy, 2019, 21(8), 729. (SCI)

[25] Yang Zhang, Limin Wang, Zhiyi Duan and Minghui Sun. Structure Learning of Bayesian Network Based on Adaptive Thresholding. Entropy, 2019, 21(7), 665. (SCI)

[26] Zhiyi Duan, Limin Wang and Minghui Sun. Model Matching: A Novel Framework to use Clustering Strategy to Solve the Classification Problem. IEEE Access, 2019, 7, 76227-76240. (SCI)

[27] Yuguang Long, Limin Wang and Minghui Sun. Structure Extension of Tree-Augmented Naive Bayes. Entropy, 2019, 21(8), 721. (SCI)

[28] Limin Wang, Yang Liu, Musa Mammadov, Minghui Sun and Sikai Qi. Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance. Entropy, 2019, 21(5), 489. (SCI)

[29] Zhiyi Duan, Limin Wang, Musa Mammadov, Hua Lou and Minghui Sun. Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data. Entropy, 2019, 21(5), 537. (SCI)

全国研究生建模大赛一等奖，太阳成集团tyc122cc“精英杯”特等奖，指导的研究生常年获得国家奖学金，指导本科生完成多项国家级、校级大创项目。

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、担任IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern recognition, Knowledge-based systems, Expert system with application, 计算机学报, 软件学报等国内外高水平期刊及知名国际会议的论文评审专家；国家自然科学基金通信评审专家。

与澳大利亚莫纳什大学和迪肯大学等国际知名高校学者保持密切的学术合作和交流，联合指导博士与硕士研究生。

硕士生进入华为、百度和阿里等国际知名公司、字节跳动研究院，博士生进入东北师大、东北电力等高校。

欢迎报考吉大计算机学院或软件学院的研究生进行联系，也欢迎优秀本科生参与主持大创项目。联系邮箱：wanglim@jlu.edu.cn。