Limin Wang

According to our database, Limin Wang authored at least 43 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learning Balanced Bayesian Classifiers From Labeled and Unlabeled Data.

Lu Guo, Limin Wang, Qilong Li, Kuo Li

IEEE Trans. Big Data, August, 2024

Learning high-dependence Bayesian network classifier with robust topology.

Limin Wang, Lingling Li, Qilong Li, Kuo Li

Expert Syst. Appl., 2024

Efficient heuristics for learning scalable Bayesian network classifier from labeled and unlabeled data.

Limin Wang, Junjie Wang, Lu Guo, Qilong Li

Appl. Intell., 2024

2023

Exploring complex multivariate probability distributions with simple and robust bayesian network topology for classification.

Lanni Wang, Limin Wang, Lu Guo, Qilong Li, Xiongfei Li

Appl. Intell., December, 2023

Selective A<i>n</i>DE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI).

Shenglei Chen, Xin Ma, Linyuan Liu, Limin Wang

J. Exp. Theor. Artif. Intell., 2023

Exploiting the implicit independence assumption for learning directed graphical models.

Limin Wang, Junyang Wei, Kuo Li, Jiaping Zhou

Intell. Data Anal., 2023

2022

Alleviating the attribute conditional independence and I.I.D. assumptions of averaged one-dependence estimator by double weighting.

Limin Wang, Yibin Xie, Meng Pang, Junyang Wei

Knowl. Based Syst., 2022

Identification of informational and probabilistic independence by adaptive thresholding.

Kuo Li, Aimin Wang, Limin Wang, Hangqi Fan, Shuai Zhang

Intell. Data Anal., 2022

Learning causal Bayesian networks based on causality analysis for classification.

Limin Wang, Jiaping Zhou, Junyang Wei, Meng Pang, Minghui Sun

Eng. Appl. Artif. Intell., 2022

Semi-supervised weighting for averaged one-dependence estimators.

Limin Wang, Shuai Zhang, Musa Mammadov, Kuo Li, Xinhao Zhang, Siyuan Wu

Appl. Intell., 2022

2021

Hierarchical Independence Thresholding for learning Bayesian network classifiers.

Yang Liu, Limin Wang, Musa Mammadov, Shenglei Chen, Gaojie Wang, Sikai Qi, Minghui Sun

Knowl. Based Syst., 2021

Bagging k-dependence Bayesian network classifiers.

Limin Wang, Sikai Qi, Yang Liu, Hua Lou, Xin Zuo

Intell. Data Anal., 2021

Alleviating the independence assumptions of averaged one-dependence estimators by model weighting.

Limin Wang, Peng Chen, Musa Mammadov, Yang Liu, Si-Yuan Wu

Intell. Data Anal., 2021

A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier.

Limin Wang, Peng Chen, Shenglei Chen, Minghui Sun

Intell. Data Anal., 2021

Averaged tree-augmented one-dependence estimators.

He Kong, Xiaohu Shi, Limin Wang, Yang Liu, Musa Mammadov, Gaojie Wang

Appl. Intell., 2021

2-Thumbs Typing: A Novel Bimanual Text Entry Method in Virtual Reality Environments.

Zigang Zhang, Minghui Sun, Boyu Gao, Limin Wang

Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2021

2020

Learning semi-lazy Bayesian network classifier under the c.i.i.d assumption.

Yang Liu, Limin Wang, Musa Mammadov

Knowl. Based Syst., 2020

Instance-based weighting filter for superparent one-dependence estimators.

Zhiyi Duan, Limin Wang, Shenglei Chen, Minghui Sun

Knowl. Based Syst., 2020

Efficient heuristics for learning Bayesian network from labeled and unlabeled data.

Zhiyi Duan, Limin Wang, Minghui Sun

Intell. Data Anal., 2020

Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators.

Limin Wang, Jie Chen, Yang Liu, Minghui Sun

IEEE Access, 2020

PhoneCursor: Improving 3D Selection Performance With Mobile Device in AR.

Minghui Sun, Mingming Cao, Limin Wang, Qian Qian

IEEE Access, 2020

Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions.

Hua Lou, Gaojie Wang, Limin Wang, Musa Mammadov

IEEE Access, 2020

2019

Structure Learning of Bayesian Network Based on Adaptive Thresholding.

Yang Zhang, Limin Wang, Zhiyi Duan, Minghui Sun

Entropy, 2019

Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance.

Limin Wang, Yang Liu, Musa Mammadov, Minghui Sun, Sikai Qi

Entropy, 2019

Structure Extension of Tree-Augmented Naive Bayes.

Yuguang Long, Limin Wang, Minghui Sun

Entropy, 2019

Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning.

Siqi Gao, Hua Lou, Limin Wang, Yang Liu, Tiehu Fan

Entropy, 2019

Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data.

Zhiyi Duan, Limin Wang, Musa Mammadov, Hua Lou, Ming-Hui Sun

Entropy, 2019

Optimizing the Topology of Bayesian Network Classifiers by Applying Conditional Entropy to Mine Causal Relationships Between Attributes.

Limin Wang, Gaojie Wang, Zhiyi Duan, Hua Lou, 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, Limin Wang

IEEE Access, 2019

Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies.

Yuguang Long, Limin Wang, Zhiyi Duan, Minghui Sun

IEEE Access, 2019

Model Matching: A Novel Framework to use Clustering Strategy to Solve the Classification Problem.

Zhiyi Duan, Limin Wang, Minghui Sun

IEEE Access, 2019

2018

Efficient Heuristics for Structure Learning of <i>k</i>-Dependence Bayesian Classifier.

Yang Liu, Limin Wang, Minghui Sun

Entropy, 2018

Investigating the effects of vibrotactile feedback on human performance in navigation tasks.

Minghui Sun, Wenzhao Gu, Limin Wang, Liyan Dong, Qian Qian

Comput. Electr. Eng., 2018

Target Learning: A Novel Framework to Mine Significant Dependencies for Unlabeled Data.

Limin Wang, Shenglei Chen, Musa Mammadov

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

2017

Sample-Based Attribute Selective A<i>n</i> DE for Large Data.

Shenglei Chen, Ana M. Martínez, Geoffrey I. Webb, Limin Wang

IEEE Trans. Knowl. Data Eng., 2017

Selective AnDE for large data learning: a low-bias memory constrained approach.

Shenglei Chen, Ana M. Martínez, Geoffrey I. Webb, Limin Wang

Knowl. Inf. Syst., 2017

Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels.

Yi Sun, Limin Wang, Minghui Sun

Entropy, 2017

<i>K</i>-Dependence Bayesian Classifier Ensemble.

Zhiyi Duan, Limin Wang

Entropy, 2017

Weighted One-Dependence Forests Classifier.

Guojing Zhong, Limin Wang

Proceedings of the Parallel Architecture, Algorithm and Programming, 2017

2016

Bayesian network classifiers based on Gaussian kernel density.

Shuang-Cheng Wang, Rui Gao, Limin Wang

Expert Syst. Appl., 2016

Learning Based K-Dependence Bayesian Classifiers.

Limin Wang, Yuanxiang Xie, Huisi Zhou, Yiming Wang, Jiangshan Guo

Proceedings of the Cloud Computing and Security - Second International Conference, 2016

2015

General and Local: Averaged k-Dependence Bayesian Classifiers.

Limin Wang, Haoyu Zhao, Minghui Sun, Yue Ning

Entropy, 2015

2014

How to Mine Information from Each Instance to Extract anAbbreviated and Credible Logical Rule.

Limin Wang, Minghui Sun, Chunhong Cao

Entropy, 2014