Hidden Naive Bayes. - Harry Zhang; Liangxiao Jiang; Jiang Su (AAAI, 2005)

One Dependence Augmented Naive Bayes. - Liangxiao Jiang; Harry Zhang; Zhihua Cai; Jiang Su (ADMA, 2005)

Learning k-Nearest Neighbor Naive Bayes for Ranking. - Liangxiao Jiang; Harry Zhang; Jiang Su (ADMA, 2005)

Instance Cloning Local Naive Bayes. - Liangxiao Jiang; Harry Zhang; Jiang Su (Canadian AI, 2005)

Learning Tree Augmented Naive Bayes for Ranking. - Liangxiao Jiang; Harry Zhang; Zhihua Cai; Jiang Su (DASFAA, 2005)

Learning Instance Greedily Cloning Naive Bayes for Ranking. - Liangxiao Jiang; Harry Zhang (ICDM, 2005)

Augmenting naive Bayes for ranking. - Harry Zhang; Liangxiao Jiang; Jiang Su (ICML, 2005)

Learning Lazy Naive Bayesian Classifiers for Ranking. - Liangxiao Jiang; Yuanyuan Guo (ICTAI, 2005)

Learning Naive Bayes for Probability Estimation by Feature Selection. - Liangxiao Jiang; Harry Zhang (Canadian AI, 2006)

Lazy Averaged One-Dependence Estimators. - Liangxiao Jiang; Harry Zhang (Canadian AI, 2006)

Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted. - Liangxiao Jiang; Harry Zhang; Zhihua Cai (FSKD, 2006)

Augmented Naive Bayes Based on Evolutional Strategy. - Dan Zeng; Sifa Zhang; Zhihua Cai; Siwei Jiang; Liangxiao Jiang (ISDA (1), 2006)

A Novel One-dependence Estimator Based on Multi-parents. - Dan Zeng; Sifa Zhang; Zhihua Cai; Siwei Jiang; Liangxiao Jiang (ISDA (1), 2006)

Weightily Averaged One-Dependence Estimators. - Liangxiao Jiang; Harry Zhang (PRICAI, 2006)

Using Locally Weighted Learning to Improve SMOreg for Regression. - Chaoqun Li 001; Liangxiao Jiang (PRICAI, 2006)

Survey of Improving Naive Bayes for Classification. - Liangxiao Jiang; Dianhong Wang; Zhihua Cai; Xuesong Yan 001 (ADMA, 2007)

Learning Locally Weighted C4.4 for Class Probability Estimation. - Liangxiao Jiang; Harry Zhang; Dianhong Wang; Zhihua Cai (Discovery Science, 2007)

Survey of Improving K-Nearest-Neighbor for Classification. - Liangxiao Jiang; Zhihua Cai; Dianhong Wang; Siwei Jiang (FSKD (1), 2007)

An Improved Attribute Selection Measure for Decision Tree Induction. - Dianhong Wang; Liangxiao Jiang (FSKD (4), 2007)

K-Distributions: A New Algorithm for Clustering Categorical Data. - Zhihua Cai; Dianhong Wang; Liangxiao Jiang (ICIC (2), 2007)

Scaling Up the Accuracy of Bayesian Network Classifiers by M-Estimate. - Liangxiao Jiang; Dianhong Wang; Zhihua Cai (ICIC (2), 2007)

A Combined Classification Algorithm Based on C4.5 and NB. - Liangxiao Jiang; Chaoqun Li 001; Jia Wu 001; Jian Zhu (ISICA, 2008)

Enhancing the performance of differential evolution using orthogonal design method. - Wenyin Gong; Zhihua Cai; Liangxiao Jiang (Appl. Math. Comput., 2008)

Using Instance cloning to Improve Naive Bayes for Ranking. - Liangxiao Jiang; Dianhong Wang; Harry Zhang; Zhihua Cai; Bo Huang 005 (Int. J. Pattern Recognit. Artif. Intell., 2008)

Decision Tree with Better Class Probability Estimation. - Liangxiao Jiang; Chaoqun Li 001; Zhihua Cai (Int. J. Pattern Recognit. Artif. Intell., 2009)

Learning decision tree for ranking. - Liangxiao Jiang; Chaoqun Li 001; Zhihua Cai (Knowl. Inf. Syst., 2009)

A Novel Bayes Model: Hidden Naive Bayes. - Liangxiao Jiang; Harry Zhang; Zhihua Cai (IEEE Trans. Knowl. Data Eng., 2009)

A novel method for inducing ID3 decision trees based on variable precision rough set. - Xingwen Liu; Dianhong Wang; Liangxiao Jiang; Fenxiong Chen; Shengfeng Gan (ICNC, 2011)

Learning random forests for ranking. - Liangxiao Jiang (Frontiers Comput. Sci. China, 2011)

Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination. - Liangxiao Jiang; Chaoqun Li 001 (J. Comput., 2011)

An Empirical Study on Class Probability Estimates in Decision Tree Learning. - Liangxiao Jiang; Chaoqun Li 001 (J. Softw., 2011)

Random one-dependence estimators. - Liangxiao Jiang (Pattern Recognit. Lett., 2011)

Not so greedy: Randomly Selected Naive Bayes. - Liangxiao Jiang; Zhihua Cai; Harry Zhang; Dianhong Wang (Expert Syst. Appl., 2012)

Discriminatively Weighted Naive Bayes and its Application in Text Classification. - Liangxiao Jiang; Dianhong Wang; Zhihua Cai (Int. J. Artif. Intell. Tools, 2012)

Rough Set Approach to Multivariate Decision Trees Inducing. - Dianhong Wang; Xingwen Liu; Liangxiao Jiang; Xiaoting Zhang; Yongguang Zhao (J. Comput., 2012)

Weighted average of one-dependence estimators†. - Liangxiao Jiang; Harry Zhang; Zhihua Cai; Dianhong Wang (J. Exp. Theor. Artif. Intell., 2012)

Learning Instance Weighted Naive Bayes from labeled and unlabeled data. - Liangxiao Jiang (J. Intell. Inf. Syst., 2012)

Improving Tree augmented Naive Bayes for class probability estimation. - Liangxiao Jiang; Zhihua Cai; Dianhong Wang; Harry Zhang (Knowl. Based Syst., 2012)

Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning. - Liangxiao Jiang; Chaoqun Li 001; Zhihua Cai; Harry Zhang (ICTAI, 2013)

Attribute Weighted Value Difference Metric. - Chaoqun Li 001; Liangxiao Jiang; Hongwei Li 003; Shasha Wang (ICTAI, 2013)

Naive Bayes text classifiers: a locally weighted learning approach. - Liangxiao Jiang; Zhihua Cai; Harry Zhang; Dianhong Wang (J. Exp. Theor. Artif. Intell., 2013)

Finding time series discord based on bit representation clustering. - Guiling Li; Olli Bräysy; Liangxiao Jiang; Zongda Wu; Yuanzhen Wang (Knowl. Based Syst., 2013)

An Augmented Value Difference Measure. - Liangxiao Jiang; Chaoqun Li 001 (Pattern Recognit. Lett., 2013)

A CFS-Based Feature Weighting Approach to Naive Bayes Text Classifiers. - Shasha Wang; Liangxiao Jiang; Chaoqun Li 001 (ICANN, 2014)

Naive Bayes for value difference metric. - Chaoqun Li 001; Liangxiao Jiang; Hongwei Li 003 (Frontiers Comput. Sci., 2014)

A Novel Distance Function: frequency difference Metric. - Liangxiao Jiang; Chaoqun Li 001; Harry Zhang; Zhihua Cai (Int. J. Pattern Recognit. Artif. Intell., 2014)

Bayesian Citation-KNN with distance weighting. - Liangxiao Jiang; Zhihua Cai; Dianhong Wang; Harry Zhang (Int. J. Mach. Learn. Cybern., 2014)

Cost-sensitive Bayesian network classifiers. - Liangxiao Jiang; Chaoqun Li 001; Shasha Wang (Pattern Recognit. Lett., 2014)

Local value difference metric. - Chaoqun Li 001; Liangxiao Jiang; Hongwei Li 003 (Pattern Recognit. Lett., 2014)

A differential evolution-based method for class-imbalanced cost-sensitive learning. - Chen Qiu 005; Liangxiao Jiang; Ganggang Kong (IJCNN, 2015)

Not always simple classification: Learning SuperParent for class probability estimation. - Chen Qiu 005; Liangxiao Jiang; Chaoqun Li 001 (Expert Syst. Appl., 2015)

A Novel Minority Cloning Technique for Cost-Sensitive Learning. - Liangxiao Jiang; Chen Qiu 005; Chaoqun Li 001 (Int. J. Pattern Recognit. Artif. Intell., 2015)

Adapting naive Bayes tree for text classification. - Shasha Wang; Liangxiao Jiang; Chaoqun Li 001 (Knowl. Inf. Syst., 2015)

C4.5 or Naive Bayes: A Discriminative Model Selection Approach. - Lungan Zhang; Liangxiao Jiang; Chaoqun Li 001 (ICANN (1), 2016)

Deep feature weighting for naive Bayes and its application to text classification. - Liangxiao Jiang; Chaoqun Li 001; Shasha Wang; Lungan Zhang (Eng. Appl. Artif. Intell., 2016)

A New Feature Selection Approach to Naive Bayes Text Classifiers. - Lungan Zhang; Liangxiao Jiang; Chaoqun Li 001 (Int. J. Pattern Recognit. Artif. Intell., 2016)

Structure extended multinomial naive Bayes. - Liangxiao Jiang; Shasha Wang; Chaoqun Li 001; Lungan Zhang (Inf. Sci., 2016)

Noise filtering to improve data and model quality for crowdsourcing. - Chaoqun Li 001; Victor S. Sheng; Liangxiao Jiang; Hongwei Li 003 (Knowl. Based Syst., 2016)

Two feature weighting approaches for naive Bayes text classifiers. - Lungan Zhang; Liangxiao Jiang; Chaoqun Li 001; Ganggang Kong (Knowl. Based Syst., 2016)

Beyond accuracy: Learning selective Bayesian classifiers with minimal test cost. - Ganggang Kong; Liangxiao Jiang; Chaoqun Li 001 (Pattern Recognit. Lett., 2016)

Randomly selected decision tree for test-cost sensitive learning. - Chen Qiu 005; Liangxiao Jiang; Chaoqun Li 001 (Appl. Soft Comput., 2017)

Attribute Value Weighted Average of One-Dependence Estimators. - Liangjun Yu; Liangxiao Jiang; Dianhong Wang; Lungan Zhang (Entropy, 2017)

Toward value difference metric with attribute weighting. - Chaoqun Li 001; Liangxiao Jiang; Hongwei Li 003; Jia Wu 001; Peng Zhang 001 (Knowl. Inf. Syst., 2017)

Weight Adjusted Naive Bayes. - Liangjun Yu; Liangxiao Jiang; Lungan Zhang; Dianhong Wang (ICTAI, 2018)

Using Differential Evolution to Estimate Labeler Quality for Crowdsourcing. - Chen Qiu 005; Liangxiao Jiang; Zhihua Cai (PRICAI, 2018)

Differential Evolution-Based Weighted Majority Voting for Crowdsourcing. - Hao Zhang; Liangxiao Jiang; Wenqiang Xu (PRICAI, 2018)

Multiple Noisy Label Distribution Propagation for Crowdsourcing. - Hao Zhang; Liangxiao Jiang; Wenqiang Xu (IJCAI, 2019)

Noise correction to improve data and model quality for crowdsourcing. - Chaoqun Li 001; Liangxiao Jiang; Wenqiang Xu (Eng. Appl. Artif. Intell., 2019)

An attribute value frequency-based instance weighting filter for naive Bayes. - Wenqiang Xu; Liangxiao Jiang; Liangjun Yu (J. Exp. Theor. Artif. Intell., 2019)

Two improved attribute weighting schemes for value difference metric. - Liangxiao Jiang; Chaoqun Li 001 (Knowl. Inf. Syst., 2019)

Toward naive Bayes with attribute value weighting. - Liangjun Yu; Liangxiao Jiang; Dianhong Wang; Lungan Zhang (Neural Comput. Appl., 2019)

A discriminative model selection approach and its application to text classification. - Lungan Zhang; Liangxiao Jiang; Chaoqun Li 001 (Neural Comput. Appl., 2019)

Class-specific attribute weighted naive Bayes. - Liangxiao Jiang; Lungan Zhang; Liangjun Yu; Dianhong Wang (Pattern Recognit., 2019)

A Correlation-Based Feature Weighting Filter for Naive Bayes. - Liangxiao Jiang; Lungan Zhang; Chaoqun Li 001; Jia Wu 001 (IEEE Trans. Knowl. Data Eng., 2019)

Class-specific attribute value weighting for Naive Bayes. - Huan Zhang 007; Liangxiao Jiang; Liangjun Yu (Inf. Sci., 2020)

Averaged one-dependence inverted specific-class distance measure for nominal attributes. - Fang Gong; Liangxiao Jiang; Dianhong Wang; Xingfeng Guo (J. Exp. Theor. Artif. Intell., 2020)

Label similarity-based weighted soft majority voting and pairing for crowdsourcing. - Fangna Tao; Liangxiao Jiang; Chaoqun Li 001 (Knowl. Inf. Syst., 2020)

Gain ratio weighted inverted specific-class distance measure for nominal attributes. - Fang Gong; Liangxiao Jiang; Huan Zhang 007; Dianhong Wang; Xingfeng Guo (Int. J. Mach. Learn. Cybern., 2020)

Using modified term frequency to improve term weighting for text classification. - Long Chen; Liangxiao Jiang; Chaoqun Li 001 (Eng. Appl. Artif. Intell., 2021)

Differential evolution-based weighted soft majority voting for crowdsourcing. - Fangna Tao; Liangxiao Jiang; Chaoqun Li 001 (Eng. Appl. Artif. Intell., 2021)

Modified DFS-based term weighting scheme for text classification. - Long Chen; Liangxiao Jiang; Chaoqun Li 001 (Expert Syst. Appl., 2021)

CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection. - Huan Zhang 007; Liangxiao Jiang; Chaoqun Li 001 (Expert Syst. Appl., 2021)

Fine-grained attribute weighted inverted specific-class distance measure for nominal attributes. - Fang Gong; Xin Wang 002; Liangxiao Jiang; Seyyed Mohammadreza Rahimi; Dianhong Wang (Inf. Sci., 2021)

Improving data and model quality in crowdsourcing using cross-entropy-based noise correction. - Wenqiang Xu; Liangxiao Jiang; Chaoqun Li 001 (Inf. Sci., 2021)

Resampling-based noise correction for crowdsourcing. - Wenqiang Xu; Liangxiao Jiang; Chaoqun Li 001 (J. Exp. Theor. Artif. Intell., 2021)

Collaboratively weighted naive Bayes. - Huan Zhang 007; Liangxiao Jiang; Chaoqun Li 001 (Knowl. Inf. Syst., 2021)

Attribute and instance weighted naive Bayes. - Huan Zhang 007; Liangxiao Jiang; Liangjun Yu (Pattern Recognit., 2021)

Wrapper Framework for Test-Cost-Sensitive Feature Selection. - Liangxiao Jiang; Ganggang Kong; Chaoqun Li 001 (IEEE Trans. Syst. Man Cybern. Syst., 2021)

A novel ground truth inference algorithm based on instance similarity for crowdsourcing learning. - Ben Ma; Chaoqun Li 001; Liangxiao Jiang (Appl. Intell., 2022)

Attribute augmented and weighted naive Bayes. - Huan Zhang 007; Liangxiao Jiang; Chaoqun Li 001 (Sci. China Inf. Sci., 2022)

Label distribution-based noise correction for multiclass crowdsourcing. - Ziqi Chen; Liangxiao Jiang; Chaoqun Li 001 (Int. J. Intell. Syst., 2022)

Fine tuning attribute weighted naive Bayes. - Huan Zhang 007; Liangxiao Jiang (Neurocomputing, 2022)

Label augmented and weighted majority voting for crowdsourcing. - Ziqi Chen; Liangxiao Jiang; Chaoqun Li 001 (Inf. Sci., 2022)

Improving data and model quality in crowdsourcing using co-training-based noise correction. - Yu Dong; Liangxiao Jiang; Chaoqun Li 001 (Inf. Sci., 2022)

Learning from crowds with decision trees. - Wenjun Yang; Chaoqun Li 001; Liangxiao Jiang (Knowl. Inf. Syst., 2022)

Learning From Crowds With Multiple Noisy Label Distribution Propagation. - Liangxiao Jiang; Hao Zhang; Fangna Tao; Chaoqun Li 001 (IEEE Trans. Neural Networks Learn. Syst., 2022)

Instance Weighting-Based Noise Correction for Crowdsourcing. - Qiang Ji; Liangxiao Jiang; Wenjun Zhang 012 (ICIC (4), 2023)

Hierarchical Attention Learning for Multimodal Classification. - Xin Zou 001; Chang Tang; Wei Zhang 049; Kun Sun 002; Liangxiao Jiang (ICME, 2023)

Label confidence-based noise correction for crowdsourcing. - Lijuan Ren; Liangxiao Jiang; Chaoqun Li 001 (Eng. Appl. Artif. Intell., 2023)

Instance difficulty-based noise correction for crowdsourcing. - Yufei Hu; Liangxiao Jiang; Chaoqun Li 001 (Expert Syst. Appl., 2023)

A multi-view-based noise correction algorithm for crowdsourcing learning. - Xinyang Li; Chaoqun Li 001; Liangxiao Jiang (Inf. Fusion, 2023)

Neighborhood Weighted Voting-Based Noise Correction for Crowdsourcing. - Huiru Li; Liangxiao Jiang; Siqing Xue (ACM Trans. Knowl. Discov. Data, 2023)

Learning from crowds with robust support vector machines. - Wenjun Yang; Chaoqun Li 001; Liangxiao Jiang (Sci. China Inf. Sci., 2023)

Dual-View Noise Correction for Crowdsourcing. - Qiang Ji; Liangxiao Jiang; Wenjun Zhang 012 (IEEE Internet Things J., 2023)

Multi-View Attribute Weighted Naive Bayes. - Huan Zhang 007; Liangxiao Jiang; Wenjun Zhang 012; Chaoqun Li 001 (IEEE Trans. Knowl. Data Eng., 2023)

Learning from crowds with robust logistic regression. - Wenbin Li; Chaoqun Li 001; Liangxiao Jiang (Inf. Sci., 2023)

Rigorous non-disjoint discretization for naive Bayes. - Huan Zhang 007; Liangxiao Jiang; Geoffrey I. Webb (Pattern Recognit., 2023)

Three-way decision-based noise correction for crowdsourcing. - Xue Wu; Liangxiao Jiang; Wenjun Zhang 012; Chaoqun Li 001 (Int. J. Approx. Reason., 2023)

Attribute augmentation-based label integration for crowdsourcing. - Yao Zhang; Liangxiao Jiang; Chaoqun Li 001 (Frontiers Comput. Sci., 2023)

Improving label quality in crowdsourcing using deep co-teaching-based noise correction. - Kang Zhu; Siqing Xue; Liangxiao Jiang (Int. J. Mach. Learn. Cybern., 2023)

Probabilistic Matrix Factorization-based Three-stage Label Completion for Crowdsourcing. - Boyi Yang; Liangxiao Jiang; Wenjun Zhang 012 (ICDM, 2024)

IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing. - Wenjun Zhang 012; Liangxiao Jiang; Chaoqun Li 001 (NeurIPS, 2024)

KFNN: K-Free Nearest Neighbor For Crowdsourcing. - Wenjun Zhang 012; Liangxiao Jiang; Chaoqun Li 001 (NeurIPS, 2024)

Label Consistency-based Worker Filtering for Crowdsourcing. - Jiao Li; Liangxiao Jiang; Chaoqun Li 001; Wenjun Zhang 012 (UAI, 2024)

Learning from Crowds with Dual-View K-Nearest Neighbor. - Jiao Li; Liangxiao Jiang; Xue Wu; Wenjun Zhang 012 (UAI, 2024)

FNNWV: farthest-nearest neighbor-based weighted voting for class-imbalanced crowdsourcing. - Wenjun Zhang 012; Liangxiao Jiang; Ziqi Chen; Chaoqun Li 001 (Sci. China Inf. Sci., 2024)

Token-disentangling Mutual Transformer for multimodal emotion recognition. - Guanghao Yin; Yuanyuan Liu 004; Tengfei Liu; Haoyu Zhang 001; Fang Fang 008; Chang Tang; Liangxiao Jiang (Eng. Appl. Artif. Intell., 2024)

Instance redistribution-based label integration for crowdsourcing. - Yao Zhang; Liangxiao Jiang; Chaoqun Li 001 (Inf. Sci., 2024)

Certainty weighted voting-based noise correction for crowdsourcing. - Huiru Li; Liangxiao Jiang; Chaoqun Li 001 (Pattern Recognit., 2024)

DDRANet: A Dynamic Density-Region-Aware Network for Crowd Counting. - Mengqi Lei; Haochen Wu; Xinhua Lv; Liangxiao Jiang (IEEE Signal Process. Lett., 2024)

Weighted Adversarial Learning From Crowds. - Ziqi Chen; Liangxiao Jiang; Wenjun Zhang 012; Chaoqun Li 001 (IEEE Trans. Serv. Comput., 2024)

Worker similarity-based noise correction for crowdsourcing. - Yufei Hu; Liangxiao Jiang; Wenjun Zhang 012 (Inf. Syst., 2024)

Label distribution similarity-based noise correction for crowdsourcing. - Lijuan Ren; Liangxiao Jiang; Wenjun Zhang 012; Chaoqun Li 001 (Frontiers Comput. Sci., 2024)

Three-way decision-based label integration for crowdsourcing. - Can Pan; Liangxiao Jiang; Chaoqun Li 001 (Pattern Recognit., 2025)

WirMAE: Learning Well-Logging Interval Representations via Masked Autoencoders for Gas Hydrate Reservoir Characterization. - Zikun Li; Zhun Zhang; Liangxiao Jiang; Zhichao Liu; Hang Zhou; Jiaxin Sun; Kyungbook Lee; Fulong Ning (IEEE Trans. Geosci. Remote. Sens., 2025)

Intent-aware Recommendation Based on Principal Component Analysis. - Yuyan Ai; Chaoqun Li 001; Liangxiao Jiang (ACM Trans. Knowl. Discov. Data, 2025)

ELDP: Enhanced Label Distribution Propagation for Crowdsourcing. - Wenjun Zhang 012; Liangxiao Jiang; Chaoqun Li 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Worker Similarity-Based Label Completion for Crowdsourcing. - Xue Wu; Liangxiao Jiang; Wenjun Zhang 012; Chaoqun Li 001 (IEEE Trans. Big Data, 2025)

Dual-View Learning from Crowds. - Huan Zhang 007; Liangxiao Jiang; Wenjun Zhang 012; Geoffrey I. Webb (ACM Trans. Knowl. Discov. Data, 2025)

Label Consistency-Based Ground Truth Inference for Crowdsourcing. - Jiao Li; Liangxiao Jiang; Wenjun Zhang 012 (IEEE Trans. Neural Networks Learn. Syst., 2025)

Confident learning-based noise correction for crowdsourcing. - Bingrui Su; Liangxiao Jiang; Shanshan Si (Pattern Recognit., 2026)