Liangxiao Jiang

Pattern Recognit., 2025

2024

Label distribution similarity-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Frontiers Comput. Sci., October, 2024

Worker similarity-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Yufei Hu

Wenjun Zhang

Inf. Syst., March, 2024

DDRANet: A Dynamic Density-Region-Aware Network for Crowd Counting.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 2024

Certainty weighted voting-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Huiru Li

Pattern Recognit., 2024

Instance redistribution-based label integration for crowdsourcing.

[BibT_eX]

[DOI]

Yao Zhang

Inf. Sci., 2024

Token-disentangling Mutual Transformer for multimodal emotion recognition.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2024

2023

Improving label quality in crowdsourcing using deep co-teaching-based noise correction.

[BibT_eX]

[DOI]

Kang Zhu

Siqing Xue

Int. J. Mach. Learn. Cybern., October, 2023

Attribute augmentation-based label integration for crowdsourcing.

[BibT_eX]

[DOI]

Yao Zhang

Frontiers Comput. Sci., October, 2023

Three-way decision-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., September, 2023

Rigorous non-disjoint discretization for naive Bayes.

[BibT_eX]

[DOI]

Geoffrey I. Webb

Pattern Recognit., August, 2023

Learning from crowds with robust logistic regression.

[BibT_eX]

[DOI]

Wenbin Li

Inf. Sci., August, 2023

Multi-View Attribute Weighted Naive Bayes.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., July, 2023

Dual-View Noise Correction for Crowdsourcing.

[BibT_eX]

[DOI]

Qiang Ji

Wenjun Zhang

IEEE Internet Things J., July, 2023

Learning from crowds with robust support vector machines.

[BibT_eX]

[DOI]

Wenjun Yang

Sci. China Inf. Sci., March, 2023

Neighborhood Weighted Voting-Based Noise Correction for Crowdsourcing.

[BibT_eX]

[DOI]

Huiru Li

Siqing Xue

ACM Trans. Knowl. Discov. Data, 2023

A multi-view-based noise correction algorithm for crowdsourcing learning.

[BibT_eX]

[DOI]

Xinyang Li

Inf. Fusion, 2023

Instance difficulty-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Yufei Hu

Expert Syst. Appl., 2023

Label confidence-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Lijuan Ren

Eng. Appl. Artif. Intell., 2023

Hierarchical Attention Learning for Multimodal Classification.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Multimedia and Expo, 2023

Instance Weighting-Based Noise Correction for Crowdsourcing.

[BibT_eX]

[DOI]

Qiang Ji

Wenjun Zhang

Proceedings of the Advanced Intelligent Computing Technology and Applications, 2023

2022

Learning From Crowds With Multiple Noisy Label Distribution Propagation.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2022

Learning from crowds with decision trees.

[BibT_eX]

[DOI]

Wenjun Yang

Knowl. Inf. Syst., 2022

Improving data and model quality in crowdsourcing using co-training-based noise correction.

[BibT_eX]

[DOI]

Yu Dong

Inf. Sci., 2022

Label augmented and weighted majority voting for crowdsourcing.

[BibT_eX]

[DOI]

Ziqi Chen

Inf. Sci., 2022

Fine tuning attribute weighted naive Bayes.

[BibT_eX]

[DOI]

Neurocomputing, 2022

Label distribution-based noise correction for multiclass crowdsourcing.

[BibT_eX]

[DOI]

Ziqi Chen

Int. J. Intell. Syst., 2022

Attribute augmented and weighted naive Bayes.

[BibT_eX]

[DOI]

Sci. China Inf. Sci., 2022

A novel ground truth inference algorithm based on instance similarity for crowdsourcing learning.

[BibT_eX]

[DOI]

Ben Ma

Appl. Intell., 2022

2021

Wrapper Framework for Test-Cost-Sensitive Feature Selection.

[BibT_eX]

[DOI]

Ganggang Kong

IEEE Trans. Syst. Man Cybern. Syst., 2021

Attribute and instance weighted naive Bayes.

[BibT_eX]

[DOI]

Liangjun Yu

Pattern Recognit., 2021

Collaboratively weighted naive Bayes.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2021

Resampling-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

J. Exp. Theor. Artif. Intell., 2021

Improving data and model quality in crowdsourcing using cross-entropy-based noise correction.

[BibT_eX]

[DOI]

Inf. Sci., 2021

Fine-grained attribute weighted inverted specific-class distance measure for nominal attributes.

[BibT_eX]

[DOI]

Fang Gong

Xin Wang

Seyyed Mohammadreza Rahimi

Inf. Sci., 2021

CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2021

Modified DFS-based term weighting scheme for text classification.

[BibT_eX]

[DOI]

Long Chen

Expert Syst. Appl., 2021

Differential evolution-based weighted soft majority voting for crowdsourcing.

[BibT_eX]

[DOI]

Fangna Tao

Eng. Appl. Artif. Intell., 2021

Using modified term frequency to improve term weighting for text classification.

[BibT_eX]

[DOI]

Long Chen

Eng. Appl. Artif. Intell., 2021

2020

Gain ratio weighted inverted specific-class distance measure for nominal attributes.

[BibT_eX]

[DOI]

Int. J. Mach. Learn. Cybern., 2020

Label similarity-based weighted soft majority voting and pairing for crowdsourcing.

[BibT_eX]

[DOI]

Fangna Tao

Knowl. Inf. Syst., 2020

Averaged one-dependence inverted specific-class distance measure for nominal attributes.

[BibT_eX]

[DOI]

J. Exp. Theor. Artif. Intell., 2020

Class-specific attribute value weighting for Naive Bayes.

[BibT_eX]

[DOI]

Liangjun Yu

Inf. Sci., 2020

2019

A Correlation-Based Feature Weighting Filter for Naive Bayes.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2019

Class-specific attribute weighted naive Bayes.

[BibT_eX]

[DOI]

Pattern Recognit., 2019

A discriminative model selection approach and its application to text classification.

[BibT_eX]

[DOI]

Lungan Zhang

Neural Comput. Appl., 2019

Toward naive Bayes with attribute value weighting.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2019

Two improved attribute weighting schemes for value difference metric.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2019

An attribute value frequency-based instance weighting filter for naive Bayes.

[BibT_eX]

[DOI]

Liangjun Yu

J. Exp. Theor. Artif. Intell., 2019

Noise correction to improve data and model quality for crowdsourcing.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2019

Multiple Noisy Label Distribution Propagation for Crowdsourcing.

[BibT_eX]

[DOI]

Hao Zhang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Differential Evolution-Based Weighted Majority Voting for Crowdsourcing.

[BibT_eX]

[DOI]

Hao Zhang

Proceedings of the PRICAI 2018: Trends in Artificial Intelligence, 2018

Using Differential Evolution to Estimate Labeler Quality for Crowdsourcing.

[BibT_eX]

[DOI]

Proceedings of the PRICAI 2018: Trends in Artificial Intelligence, 2018

Weight Adjusted Naive Bayes.

[BibT_eX]

[DOI]

Proceedings of the IEEE 30th International Conference on Tools with Artificial Intelligence, 2018

2017

Toward value difference metric with attribute weighting.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2017

Attribute Value Weighted Average of One-Dependence Estimators.

[BibT_eX]

[DOI]

Entropy, 2017

Randomly selected decision tree for test-cost sensitive learning.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2017

2016

Beyond accuracy: Learning selective Bayesian classifiers with minimal test cost.

[BibT_eX]

[DOI]

Ganggang Kong

Pattern Recognit. Lett., 2016

Two feature weighting approaches for naive Bayes text classifiers.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

Noise filtering to improve data and model quality for crowdsourcing.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

Structure extended multinomial naive Bayes.

[BibT_eX]

[DOI]

Inf. Sci., 2016

A New Feature Selection Approach to Naive Bayes Text Classifiers.

[BibT_eX]

[DOI]

Lungan Zhang

Int. J. Pattern Recognit. Artif. Intell., 2016

Deep feature weighting for naive Bayes and its application to text classification.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2016

C4.5 or Naive Bayes: A Discriminative Model Selection Approach.

[BibT_eX]

[DOI]

Lungan Zhang

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

2015

Adapting naive Bayes tree for text classification.

[BibT_eX]

[DOI]

Shasha Wang

Knowl. Inf. Syst., 2015

A Novel Minority Cloning Technique for Cost-Sensitive Learning.

[BibT_eX]

[DOI]

Int. J. Pattern Recognit. Artif. Intell., 2015

Not always simple classification: Learning SuperParent for class probability estimation.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2015

A differential evolution-based method for class-imbalanced cost-sensitive learning.

[BibT_eX]

[DOI]

Ganggang Kong

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014

Local value difference metric.

[BibT_eX]

[DOI]

Hongwei Li

Pattern Recognit. Lett., 2014

Cost-sensitive Bayesian network classifiers.

[BibT_eX]

[DOI]

Shasha Wang

Pattern Recognit. Lett., 2014

Bayesian Citation-KNN with distance weighting.

[BibT_eX]

[DOI]

Int. J. Mach. Learn. Cybern., 2014

A Novel Distance Function: frequency difference Metric.

[BibT_eX]

[DOI]

Int. J. Pattern Recognit. Artif. Intell., 2014

Naive Bayes for value difference metric.

[BibT_eX]

[DOI]

Hongwei Li

Frontiers Comput. Sci., 2014

A CFS-Based Feature Weighting Approach to Naive Bayes Text Classifiers.

[BibT_eX]

[DOI]

Shasha Wang

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

2013

An Augmented Value Difference Measure.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2013

Finding time series discord based on bit representation clustering.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2013

Naive Bayes text classifiers: a locally weighted learning approach.

[BibT_eX]

[DOI]

J. Exp. Theor. Artif. Intell., 2013

Attribute Weighted Value Difference Metric.

[BibT_eX]

[DOI]

Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, 2013

Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning.

[BibT_eX]

[DOI]

Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, 2013

2012

Improving Tree augmented Naive Bayes for class probability estimation.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2012

Learning Instance Weighted Naive Bayes from labeled and unlabeled data.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., 2012

Weighted average of one-dependence estimators†.

[BibT_eX]

[DOI]

J. Exp. Theor. Artif. Intell., 2012

Rough Set Approach to Multivariate Decision Trees Inducing.

[BibT_eX]

[DOI]

J. Comput., 2012

Discriminatively Weighted Naive Bayes and its Application in Text Classification.

[BibT_eX]

[DOI]

Int. J. Artif. Intell. Tools, 2012

Not so greedy: Randomly Selected Naive Bayes.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2012

2011

Random one-dependence estimators.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2011

An Empirical Study on Class Probability Estimates in Decision Tree Learning.

[BibT_eX]

[DOI]

J. Softw., 2011

Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination.

[BibT_eX]

[DOI]

J. Comput., 2011

Learning random forests for ranking.

[BibT_eX]

[DOI]

Frontiers Comput. Sci. China, 2011

A novel method for inducing ID3 decision trees based on variable precision rough set.

[BibT_eX]

[DOI]

Proceedings of the Seventh International Conference on Natural Computation, 2011

2009

A Novel Bayes Model: Hidden Naive Bayes.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2009

Learning decision tree for ranking.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2009

Decision Tree with Better Class Probability Estimation.

[BibT_eX]

[DOI]

Int. J. Pattern Recognit. Artif. Intell., 2009

2008

Using Instance cloning to Improve Naive Bayes for Ranking.

[BibT_eX]

[DOI]

Int. J. Pattern Recognit. Artif. Intell., 2008

Enhancing the performance of differential evolution using orthogonal design method.

[BibT_eX]

[DOI]

Wenyin Gong

Appl. Math. Comput., 2008

A Combined Classification Algorithm Based on C4.5 and NB.

[BibT_eX]

[DOI]

Proceedings of the Advances in Computation and Intelligence, Third International Symposium, 2008

2007

Scaling Up the Accuracy of Bayesian Network Classifiers by M-Estimate.

[BibT_eX]

[DOI]

Proceedings of the Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 2007

K-Distributions: A New Algorithm for Clustering Categorical Data.

[BibT_eX]

[DOI]

Proceedings of the Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 2007

An Improved Attribute Selection Measure for Decision Tree Induction.

[BibT_eX]

[DOI]

Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, 2007

Survey of Improving K-Nearest-Neighbor for Classification.

[BibT_eX]

[DOI]

Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, 2007

Learning Locally Weighted C4.4 for Class Probability Estimation.

[BibT_eX]

[DOI]

Proceedings of the Discovery Science, 10th International Conference, 2007

Survey of Improving Naive Bayes for Classification.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications, Third International Conference, 2007

2006

Using Locally Weighted Learning to Improve SMOreg for Regression.

[BibT_eX]

[DOI]

Proceedings of the PRICAI 2006: Trends in Artificial Intelligence, 2006

Weightily Averaged One-Dependence Estimators.

[BibT_eX]

[DOI]

Proceedings of the PRICAI 2006: Trends in Artificial Intelligence, 2006

A Novel One-dependence Estimator Based on Multi-parents.

[BibT_eX]

[DOI]

Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA 2006), 2006

Augmented Naive Bayes Based on Evolutional Strategy.

[BibT_eX]

[DOI]

Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA 2006), 2006

Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted.

[BibT_eX]

[DOI]

Proceedings of the Fuzzy Systems and Knowledge Discovery, Third International Conference, 2006

Lazy Averaged One-Dependence Estimators.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2006

Learning Naive Bayes for Probability Estimation by Feature Selection.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2006

2005

Learning Lazy Naive Bayesian Classifiers for Ranking.

[BibT_eX]

[DOI]

Yuanyuan Guo

Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), 2005

Augmenting naive Bayes for ranking.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning, 2005

Learning Instance Greedily Cloning Naive Bayes for Ranking.

[BibT_eX]

[DOI]

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

Learning Tree Augmented Naive Bayes for Ranking.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2005

Instance Cloning Local Naive Bayes.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2005

Learning <i>k</i>-Nearest Neighbor Naive Bayes for Ranking.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications, First International Conference, 2005

One Dependence Augmented Naive Bayes.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications, First International Conference, 2005

Hidden Naive Bayes.

[BibT_eX]

[DOI]