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[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.
[9] LiMin Wang, GuoFeng Yao. Extracting Logical Rules and Attribute Subset from Confidence Domain. Information: An International Interdisciplinary Journal, 2012, 15(1), 173-180.
[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]LiMin Wang. Towards Efficient Dimensionality Reduction for Evolving Bayesian Network Classifier. Advanced Materials Research, 2010, 108-111: 240-243. (EI)
[15]LiMin Wang. An Adaptive Ensemble Approach for Multi-level Semantic Knowledge Representation. Journal of Information & Computational Science, 2010, 7(1): 9-15. (EI)
[16]LiMin Wang. Class Dependent Feature Scaling Method via Restrictive Bayesian Network Classifier Combination. Journal of Computational Information Systems, 2010, 6(1): 33-38. (EI)
[17] 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 )
[18]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 )
[19]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 )
[20]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)
[21]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)
[22]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)
[23]Wang LiMin, Cao ChunHong, Dong LiYan, Li XiaoLin. Generalized Tree Augmented Naive Bayes. Journal of Computational Information Systems, 1(4): 741-747, 2005. (EI)
[24]Wang LiMin, Li XiaoLin, Cao ChunHong, Yuan SenMiao. Combining Decision Tree and Naive Bayes for Classification. Knowledge-Based Systems, 10: 511-515, 2005. (SCI)
[25]Wang LiMin, Yuan SenMiao. Induction of hybrid decision tree based on post discretization strategy. Progress in Natural Science, 16: 541-545, 2004. (SCI)
[26]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)
[27]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)
[28]Shuangcheng Wang, Rui Gao, LiMin Wang. Bayesian network classifiers based on Gaussian kernel density. Expert Systems with Applications, 51:207-217, 2016. (SCI)
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Jilin University