Chaoqun Li

Pattern Recognit., 2025

2024

Label distribution similarity-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Frontiers Comput. Sci., October, 2024

Weighted Adversarial Learning From Crowds.

[BibT_eX]

[DOI]

IEEE Trans. Serv. Comput., 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

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

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

Learning from crowds with robust support vector machines.

[BibT_eX]

[DOI]

Wenjun Yang

Sci. China Inf. Sci., March, 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

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

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]

Huan Zhang

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

Improving crowd labeling using Stackelberg models.

[BibT_eX]

[DOI]

Wenjun Yang

Int. J. Mach. Learn. Cybern., 2021

Collaboratively weighted naive Bayes.

[BibT_eX]

[DOI]

Huan Zhang

Knowl. Inf. Syst., 2021

Resampling-based noise correction for crowdsourcing.

[BibT_eX]

[DOI]

Wenqiang Xu

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

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

[BibT_eX]

[DOI]

Wenqiang Xu

Inf. Sci., 2021

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

[BibT_eX]

[DOI]

Huan Zhang

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

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

[BibT_eX]

[DOI]

Fangna Tao

Knowl. Inf. Syst., 2020

Class-Specific Deep Feature Weighting for Naïve Bayes Text Classifiers.

[BibT_eX]

[DOI]

IEEE Access, 2020

2019

A Correlation-Based Feature Weighting Filter for Naive Bayes.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2019

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

[BibT_eX]

[DOI]

Lungan Zhang

Neural Comput. Appl., 2019

Two improved attribute weighting schemes for value difference metric.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2019

Noise correction to improve data and model quality for crowdsourcing.

[BibT_eX]

[DOI]

Wenqiang Xu

Eng. Appl. Artif. Intell., 2019

2017

Toward value difference metric with attribute weighting.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2017

Randomly selected decision tree for test-cost sensitive learning.

[BibT_eX]

[DOI]

Chen Qiu

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]

Chen Qiu

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

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

[BibT_eX]

[DOI]

Chen Qiu

Expert Syst. Appl., 2015

2014

Local value difference metric.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2014

Cost-sensitive Bayesian network classifiers.

[BibT_eX]

[DOI]

Shasha Wang

Pattern Recognit. Lett., 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]

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

Bayesian network classifiers for probability-based metrics.

[BibT_eX]

[DOI]

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

Selective Value Difference Metric.

[BibT_eX]

[DOI]

J. Comput., 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

A Modified Short and Fukunaga Metric based on the attribute independence assumption.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2012

2011

One Dependence Value Difference Metric.

[BibT_eX]

[DOI]

Knowl. Based Syst., 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

2010

A Survey of Distance Metrics for Nominal Attributes.

[BibT_eX]

[DOI]

J. Softw., 2010

An Improved Instance Weighted Linear Regression.

[BibT_eX]

[DOI]

J. Convergence Inf. Technol., 2010

2009

Learning decision tree for ranking.

[BibT_eX]

[DOI]

Zhihua Cai

Knowl. Inf. Syst., 2009

Decision Tree with Better Class Probability Estimation.

[BibT_eX]

[DOI]

Zhihua Cai

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

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

2006

Using Locally Weighted Learning to Improve SMOreg for Regression.

[BibT_eX]

[DOI]