Improving the Performance of Decision Tree: A Hybrid Approach. - Limin Wang 007; Senmiao Yuan; Ling Li; Haijun Li (ER, 2004)

Orthogonally Rotational Transformation for Naive Bayes Learning. - Limin Wang 007; Chunhong Cao; Haijun Li; Haixia Chen; Liyan Dong (CIS (1), 2005)

The Application of the Genetic algorithm-Ant algorithm in the Geometric Constraint SatisfactionGuidelines. - Chunhong Cao; Bin Zhang 001; Limin Wang 007; Wenhui Li 002 (IEEE ICCI, 2006)

Flexible Neural Tree for Pattern Recognition. - Haijun Li; Zheng-Xuan Wang; Limin Wang 007; Senmiao Yuan (ISNN (1), 2006)

The Parametric Design Based on Organizational Evolutionary Algorithm. - Chunhong Cao; Bin Zhang 001; Limin Wang 007; Wenhui Li 002 (PRICAI, 2006)

Combining decision tree and Naive Bayes for classification. - Li-Min Wang 007; Xiao-Lin Li 001; Chunhong Cao; Senmiao Yuan (Knowl. Based Syst., 2006)

Finding the Optimal Feature Representations for Bayesian Network Learning. - LiMin Wang 007; Chunhong Cao; Xiongfei Li; Haijun Li (PAKDD, 2007)

Inference and learning in hybrid probabilistic network. - Limin Wang 007; Xuecheng Wang; Xiongfei Li (Frontiers Comput. Sci. China, 2007)

Research on Decision Forest Learning Algorithm. - Limin Wang 007; Xiongfei Li (Comput. Inf. Sci., 2008)

Exploring Logical Rules Based on Causal Semantics Analysis of Relational Data. - YaoFu Cao; Limin Wang 007; Xin Zuo (JCAI, 2009)

The Geometric Constraint Solving Based on the Quantum Particle Swarm. - Chunhong Cao; Limin Wang 007; Wenhui Li 002 (RSKT, 2010)

Induction of a Novel Hybrid Decision Forest Model based on Information Theory. - Limin Wang 007; Xuebai Zang; Peijuan Xu (J. Softw., 2010)

Implementation of a scalable decision forest model based on information theory. - Limin Wang 007; Xuebai Zang (Expert Syst. Appl., 2011)

How to Mine Information from Each Instance to Extract anAbbreviated and Credible Logical Rule. - Limin Wang 007; Minghui Sun; Chunhong Cao (Entropy, 2014)

Learning a Flexible K-Dependence Bayesian Classifier from the Chain Rule of Joint Probability Distribution. - Limin Wang 007; Haoyu Zhao (Entropy, 2015)

General and Local: Averaged k-Dependence Bayesian Classifiers. - Limin Wang 007; Haoyu Zhao; Minghui Sun; Yue Ning 002 (Entropy, 2015)

Learning Based K-Dependence Bayesian Classifiers. - Limin Wang 007; Yuanxiang Xie; Huisi Zhou; Yiming Wang; Jiangshan Guo (ICCCS (2), 2016)

Bayesian network classifiers based on Gaussian kernel density. - Shuang-Cheng Wang; Rui Gao; Limin Wang 007 (Expert Syst. Appl., 2016)

Weighted One-Dependence Forests Classifier. - Guojing Zhong; Limin Wang 007 (PAAP, 2017)

K-Dependence Bayesian Classifier Ensemble. - Zhiyi Duan; Limin Wang 007 (Entropy, 2017)

Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels. - Yi Sun; Limin Wang 007; Minghui Sun (Entropy, 2017)

Selective AnDE for large data learning: a low-bias memory constrained approach. - Shenglei Chen; Ana M. Martínez; Geoffrey I. Webb; Limin Wang 007 (Knowl. Inf. Syst., 2017)

Sample-Based Attribute Selective An DE for Large Data. - Shenglei Chen; Ana M. Martínez; Geoffrey I. Webb; Limin Wang 007 (IEEE Trans. Knowl. Data Eng., 2017)

Target Learning: A Novel Framework to Mine Significant Dependencies for Unlabeled Data. - Limin Wang 007; Shenglei Chen; Musa Mammadov (PAKDD (1), 2018)

Investigating the effects of vibrotactile feedback on human performance in navigation tasks. - Minghui Sun; Wenzhao Gu; Limin Wang 007; Liyan Dong; Qian Qian (Comput. Electr. Eng., 2018)

Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier. - Yang Liu 170; Limin Wang 007; Minghui Sun (Entropy, 2018)

Model Matching: A Novel Framework to use Clustering Strategy to Solve the Classification Problem. - Zhiyi Duan; Limin Wang 007; Minghui Sun (IEEE Access, 2019)

Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies. - Yuguang Long; Limin Wang 007; Zhiyi Duan; Minghui Sun (IEEE Access, 2019)

"Watch Your Step": Precise Obstacle Detection and Navigation for Mobile Users Through Their Mobile Service. - Minghui Sun; Pengcheng Ding; Jiageng Song; Miao Song 004; Limin Wang 007 (IEEE Access, 2019)

Optimizing the Topology of Bayesian Network Classifiers by Applying Conditional Entropy to Mine Causal Relationships Between Attributes. - Limin Wang 007; Gaojie Wang; Zhiyi Duan; Hua Lou; Minghui Sun (IEEE Access, 2019)

Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data. - Zhiyi Duan; Limin Wang 007; Musa Mammadov; Hua Lou; Ming-Hui Sun (Entropy, 2019)

Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning. - Siqi Gao; Hua Lou; Limin Wang 007; Yang Liu 170; Tiehu Fan (Entropy, 2019)

Structure Extension of Tree-Augmented Naive Bayes. - Yuguang Long; Limin Wang 007; Minghui Sun (Entropy, 2019)

Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance. - Limin Wang 007; Yang Liu 170; Musa Mammadov; Minghui Sun; Sikai Qi (Entropy, 2019)

Structure Learning of Bayesian Network Based on Adaptive Thresholding. - Yang Zhang 038; Limin Wang 007; Zhiyi Duan; Minghui Sun (Entropy, 2019)

How Much Do Emotional, Behavioral, and Cognitive Factors Actually Impact College Student Attitudes towards English Language Learning? A Quantitative and Qualitative Study. - Xin Zuo; Regina Weaver; Julia MacRae; Limin Wang 007 (Inf., 2019)

Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions. - Hua Lou; Gaojie Wang; Limin Wang 007; Musa Mammadov (IEEE Access, 2020)

PhoneCursor: Improving 3D Selection Performance With Mobile Device in AR. - Minghui Sun; Mingming Cao; Limin Wang 007; Qian Qian (IEEE Access, 2020)

Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators. - Limin Wang 007; Jie Chen 066; Yang Liu 170; Minghui Sun (IEEE Access, 2020)

Efficient heuristics for learning Bayesian network from labeled and unlabeled data. - Zhiyi Duan; Limin Wang 007; Minghui Sun (Intell. Data Anal., 2020)

Instance-based weighting filter for superparent one-dependence estimators. - Zhiyi Duan; Limin Wang 007; Shenglei Chen; Minghui Sun (Knowl. Based Syst., 2020)

Learning semi-lazy Bayesian network classifier under the c.i.i.d assumption. - Yang Liu 170; Limin Wang 007; Musa Mammadov (Knowl. Based Syst., 2020)

2-Thumbs Typing: A Novel Bimanual Text Entry Method in Virtual Reality Environments. - Zigang Zhang; Minghui Sun; Boyu Gao 003; Limin Wang 007 (VR Workshops, 2021)

Averaged tree-augmented one-dependence estimators. - He Kong; Xiaohu Shi; Limin Wang 007; Yang Liu 170; Musa Mammadov; Gaojie Wang (Appl. Intell., 2021)

A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier. - Limin Wang 007; Peng Chen; Shenglei Chen; Minghui Sun (Intell. Data Anal., 2021)

Alleviating the independence assumptions of averaged one-dependence estimators by model weighting. - Limin Wang 007; Peng Chen; Musa Mammadov; Yang Liu 170; Si-Yuan Wu (Intell. Data Anal., 2021)

Bagging k-dependence Bayesian network classifiers. - Limin Wang 007; Sikai Qi; Yang Liu 170; Hua Lou; Xin Zuo (Intell. Data Anal., 2021)

Hierarchical Independence Thresholding for learning Bayesian network classifiers. - Yang Liu 170; Limin Wang 007; Musa Mammadov; Shenglei Chen; Gaojie Wang; Sikai Qi; Minghui Sun (Knowl. Based Syst., 2021)

Stochastic optimization for bayesian network classifiers. - Yi Ren; LiMin Wang 007; Xiongfei Li; Meng Pang; Junyang Wei (Appl. Intell., 2022)

Semi-supervised learning for k-dependence Bayesian classifiers. - LiMin Wang 007; XinHao Zhang 001; Kuo Li; Shuai Zhang (Appl. Intell., 2022)

Semi-supervised weighting for averaged one-dependence estimators. - Limin Wang 007; Shuai Zhang; Musa Mammadov; Kuo Li; Xinhao Zhang 001; Siyuan Wu 002 (Appl. Intell., 2022)

Learning causal Bayesian networks based on causality analysis for classification. - Limin Wang 007; Jiaping Zhou; Junyang Wei; Meng Pang; Minghui Sun (Eng. Appl. Artif. Intell., 2022)

Identification of informational and probabilistic independence by adaptive thresholding. - Kuo Li; Aimin Wang; Limin Wang 007; Hangqi Fan; Shuai Zhang (Intell. Data Anal., 2022)

Alleviating the attribute conditional independence and I.I.D. assumptions of averaged one-dependence estimator by double weighting. - Limin Wang 007; Yibin Xie; Meng Pang; Junyang Wei (Knowl. Based Syst., 2022)

Exploiting the implicit independence assumption for learning directed graphical models. - Limin Wang 007; Junyang Wei; Kuo Li; Jiaping Zhou (Intell. Data Anal., 2023)

Selective AnDE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI). - Shenglei Chen; Xin Ma 003; Linyuan Liu; Limin Wang 007 (J. Exp. Theor. Artif. Intell., 2023)

Exploring complex multivariate probability distributions with simple and robust bayesian network topology for classification. - Lanni Wang; Limin Wang 007; Lu Guo; Qilong Li; Xiongfei Li (Appl. Intell., 2023)

Efficient heuristics for learning scalable Bayesian network classifier from labeled and unlabeled data. - Limin Wang 007; Junjie Wang; Lu Guo; Qilong Li (Appl. Intell., 2024)

Learning high-dependence Bayesian network classifier with robust topology. - Limin Wang 007; Lingling Li; Qilong Li; Kuo Li (Expert Syst. Appl., 2024)

Learning Balanced Bayesian Classifiers From Labeled and Unlabeled Data. - Lu Guo; Limin Wang 007; Qilong Li; Kuo Li (IEEE Trans. Big Data, 2024)

Probability knowledge acquisition from unlabeled instance based on dual learning. - Yuetan Zhao; Limin Wang 007; Xinyu Zhu; Taosheng Jin; Minghui Sun; Xiongfei Li (Knowl. Inf. Syst., 2025)