王利民

機器學習，大數據挖掘，貝葉斯網絡，概率因果推理

面向對象數據庫，數據庫安全，數據庫原理

工作經曆：

2012年-2013年，澳大利亞莫納什大學 研究員

科研項目：

（1）國家重點研發計劃項目“場地土壤污染成因與治理技術”（No. 2019YFC1804804），2020.1-2022.12。

（2）吉林省自然科學基金“基于貝葉斯網絡的圖式知識表達和因果推理關鍵問題研究”（No. 20200201281JC），2020.1-2022.12。

（3）吉林省自然科學基金“面向海量數據深度挖掘的無約束貝葉斯網絡分類模型研究（高性能計算）”（No. 20150101014JC），2015.1-2017.12。

（4）國家自然科學基金“面向關系數據庫知識發現的概率邏輯貝葉斯網絡研究”（No. 61272209），2013.1-2016.12。

（5）國家自然科學基金項目“面向智能信息處理的貝葉斯網絡關鍵理論與方法”（No. 60275026），2003.1-2005.12。

（6）國家科技支撐計劃項目“省級應急平台和城市應急聯動技術研發與示範（吉林省）”（No. 2006BAK01A33），2006.11-2008.12。

學術論文：

在國内外權威學術期刊 TKDB、KBS、AI、IDA 和《計算機學報》發表相關學術論文60餘篇。近三年發表的文章目錄如下：

[1] 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. (SCI)

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

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

[4] 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. (SCI)

[5] 劉洋，王利民，孫銘會. 基于信息熵函數的啟發式貝葉斯因果推理. 計算機學報，2021, 44, 2135-2147. (EI)

[6] 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. (SCI)

[7] 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)

[8] 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. (SCI)

[9] 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)

[10] 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. (SCI)

[11] 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)

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

[13] 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)

[14] 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)

[15] 王利民, 姜漢民. 強化屬性依賴關系的K階貝葉斯分類模型. 控制與決策, 2019, 34(6), 1234-1240. (EI)

[16] 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)

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

[18] 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)

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

[20] 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)

[21] 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)

全國研究生建模大賽一等獎，伟德国际BETVlCTOR“精英杯”特等獎，指導的研究生常年獲得國家獎學金，指導本科生完成多項國家級、校級大創項目。

中國計算機學會（CCF）高級會員；中國人工智能學會不确定性人工智能專業委員會委員、擔任IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern recognition, Knowledge-based systems, Expert system with application, 計算機學報, 軟件學報等國内外高水平期刊及知名國際會議的論文評審專家；國家自然科學基金通信評審專家。

與澳大利亞莫納什大學和迪肯大學等國際知名高校學者保持密切的學術⏭➰和交流，聯合指導博士與碩士研究生。

碩士生進入華為、百度和阿裡等國際知名公司、字節跳動研究院，博士生進入東北師大、東北電力等高校。

歡迎報考吉大計算機學院或軟件學院的研究生進行聯系，也歡迎優秀本科生參與主持大創項目。聯系郵箱：wanglim@jlu.edu.cn。