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Liangxiao Jiang
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2020 – today
- 2024
- [j81]Guanghao Yin
, Yuanyuan Liu, Tengfei Liu, Haoyu Zhang, Fang Fang, Chang Tang, Liangxiao Jiang
:
Token-disentangling Mutual Transformer for multimodal emotion recognition. Eng. Appl. Artif. Intell. 133: 108348 (2024) - [j80]Lijuan Ren, Liangxiao Jiang
, Wenjun Zhang, Chaoqun Li:
Label distribution similarity-based noise correction for crowdsourcing. Frontiers Comput. Sci. 18(5): 185323 (2024) - [j79]Yufei Hu, Liangxiao Jiang
, Wenjun Zhang:
Worker similarity-based noise correction for crowdsourcing. Inf. Syst. 121: 102321 (2024) - [j78]Yao Zhang
, Liangxiao Jiang
, Chaoqun Li
:
Instance redistribution-based label integration for crowdsourcing. Inf. Sci. 674: 120702 (2024) - [j77]Huiru Li
, Liangxiao Jiang
, Chaoqun Li:
Certainty weighted voting-based noise correction for crowdsourcing. Pattern Recognit. 150: 110325 (2024) - 2023
- [j76]Wenjun Yang, Chaoqun Li, Liangxiao Jiang
:
Learning from crowds with robust support vector machines. Sci. China Inf. Sci. 66(3) (2023) - [j75]Lijuan Ren, Liangxiao Jiang
, Chaoqun Li:
Label confidence-based noise correction for crowdsourcing. Eng. Appl. Artif. Intell. 117(Part): 105624 (2023) - [j74]Yufei Hu, Liangxiao Jiang
, Chaoqun Li:
Instance difficulty-based noise correction for crowdsourcing. Expert Syst. Appl. 212: 118794 (2023) - [j73]Yao Zhang, Liangxiao Jiang
, Chaoqun Li:
Attribute augmentation-based label integration for crowdsourcing. Frontiers Comput. Sci. 17(5): 175331 (2023) - [j72]Xue Wu, Liangxiao Jiang
, Wenjun Zhang, Chaoqun Li:
Three-way decision-based noise correction for crowdsourcing. Int. J. Approx. Reason. 160: 108973 (2023) - [j71]Xinyang Li, Chaoqun Li, Liangxiao Jiang
:
A multi-view-based noise correction algorithm for crowdsourcing learning. Inf. Fusion 91: 529-541 (2023) - [j70]Qiang Ji
, Liangxiao Jiang
, Wenjun Zhang:
Dual-View Noise Correction for Crowdsourcing. IEEE Internet Things J. 10(13): 11804-11812 (2023) - [j69]Wenbin Li, Chaoqun Li
, Liangxiao Jiang
:
Learning from crowds with robust logistic regression. Inf. Sci. 639: 119010 (2023) - [j68]Kang Zhu, Siqing Xue, Liangxiao Jiang
:
Improving label quality in crowdsourcing using deep co-teaching-based noise correction. Int. J. Mach. Learn. Cybern. 14(10): 3641-3654 (2023) - [j67]Huan Zhang
, Liangxiao Jiang
, Geoffrey I. Webb
:
Rigorous non-disjoint discretization for naive Bayes. Pattern Recognit. 140: 109554 (2023) - [j66]Huiru Li
, Liangxiao Jiang
, Siqing Xue
:
Neighborhood Weighted Voting-Based Noise Correction for Crowdsourcing. ACM Trans. Knowl. Discov. Data 17(7): 96:1-96:18 (2023) - [j65]Huan Zhang
, Liangxiao Jiang
, Wenjun Zhang, Chaoqun Li
:
Multi-View Attribute Weighted Naive Bayes. IEEE Trans. Knowl. Data Eng. 35(7): 7291-7302 (2023) - [c34]Qiang Ji, Liangxiao Jiang, Wenjun Zhang:
Instance Weighting-Based Noise Correction for Crowdsourcing. ICIC (4) 2023: 285-297 - [c33]Xin Zou
, Chang Tang, Wei Zhang, Kun Sun, Liangxiao Jiang
:
Hierarchical Attention Learning for Multimodal Classification. ICME 2023: 936-941 - 2022
- [j64]Ben Ma, Chaoqun Li
, Liangxiao Jiang
:
A novel ground truth inference algorithm based on instance similarity for crowdsourcing learning. Appl. Intell. 52(15): 17784-17796 (2022) - [j63]Huan Zhang
, Liangxiao Jiang
, Chaoqun Li:
Attribute augmented and weighted naive Bayes. Sci. China Inf. Sci. 65(12) (2022) - [j62]Ziqi Chen
, Liangxiao Jiang
, Chaoqun Li
:
Label distribution-based noise correction for multiclass crowdsourcing. Int. J. Intell. Syst. 37(9): 5752-5767 (2022) - [j61]Huan Zhang
, Liangxiao Jiang
:
Fine tuning attribute weighted naive Bayes. Neurocomputing 488: 402-411 (2022) - [j60]Yu Dong, Liangxiao Jiang
, Chaoqun Li:
Improving data and model quality in crowdsourcing using co-training-based noise correction. Inf. Sci. 583: 174-188 (2022) - [j59]Ziqi Chen, Liangxiao Jiang
, Chaoqun Li:
Label augmented and weighted majority voting for crowdsourcing. Inf. Sci. 606: 397-409 (2022) - [j58]Wenjun Yang, Chaoqun Li
, Liangxiao Jiang
:
Learning from crowds with decision trees. Knowl. Inf. Syst. 64(8): 2123-2140 (2022) - [j57]Liangxiao Jiang
, Hao Zhang, Fangna Tao, Chaoqun Li
:
Learning From Crowds With Multiple Noisy Label Distribution Propagation. IEEE Trans. Neural Networks Learn. Syst. 33(11): 6558-6568 (2022) - 2021
- [j56]Long Chen, Liangxiao Jiang
, Chaoqun Li:
Using modified term frequency to improve term weighting for text classification. Eng. Appl. Artif. Intell. 101: 104215 (2021) - [j55]Fangna Tao, Liangxiao Jiang
, Chaoqun Li:
Differential evolution-based weighted soft majority voting for crowdsourcing. Eng. Appl. Artif. Intell. 106: 104474 (2021) - [j54]Long Chen, Liangxiao Jiang
, Chaoqun Li:
Modified DFS-based term weighting scheme for text classification. Expert Syst. Appl. 168: 114438 (2021) - [j53]Huan Zhang
, Liangxiao Jiang
, Chaoqun Li:
CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection. Expert Syst. Appl. 185: 115673 (2021) - [j52]Wenqiang Xu, Liangxiao Jiang
, Chaoqun Li:
Improving data and model quality in crowdsourcing using cross-entropy-based noise correction. Inf. Sci. 546: 803-814 (2021) - [j51]Fang Gong, Xin Wang, Liangxiao Jiang
, Seyyed Mohammadreza Rahimi, Dianhong Wang:
Fine-grained attribute weighted inverted specific-class distance measure for nominal attributes. Inf. Sci. 578: 848-869 (2021) - [j50]Wenqiang Xu, Liangxiao Jiang
, Chaoqun Li:
Resampling-based noise correction for crowdsourcing. J. Exp. Theor. Artif. Intell. 33(6): 985-999 (2021) - [j49]Huan Zhang
, Liangxiao Jiang
, Chaoqun Li:
Collaboratively weighted naive Bayes. Knowl. Inf. Syst. 63(12): 3159-3182 (2021) - [j48]Huan Zhang
, Liangxiao Jiang
, Liangjun Yu:
Attribute and instance weighted naive Bayes. Pattern Recognit. 111: 107674 (2021) - [j47]Liangxiao Jiang
, Ganggang Kong, Chaoqun Li
:
Wrapper Framework for Test-Cost-Sensitive Feature Selection. IEEE Trans. Syst. Man Cybern. Syst. 51(3): 1747-1756 (2021) - 2020
- [j46]Huan Zhang
, Liangxiao Jiang
, Liangjun Yu:
Class-specific attribute value weighting for Naive Bayes. Inf. Sci. 508: 260-274 (2020) - [j45]Fang Gong, Liangxiao Jiang
, Dianhong Wang, Xingfeng Guo:
Averaged one-dependence inverted specific-class distance measure for nominal attributes. J. Exp. Theor. Artif. Intell. 32(4): 651-663 (2020) - [j44]Fangna Tao, Liangxiao Jiang
, Chaoqun Li:
Label similarity-based weighted soft majority voting and pairing for crowdsourcing. Knowl. Inf. Syst. 62(7): 2521-2538 (2020) - [j43]Fang Gong, Liangxiao Jiang
, Huan Zhang
, Dianhong Wang, Xingfeng Guo:
Gain ratio weighted inverted specific-class distance measure for nominal attributes. Int. J. Mach. Learn. Cybern. 11(10): 2237-2246 (2020)
2010 – 2019
- 2019
- [j42]Chaoqun Li, Liangxiao Jiang
, Wenqiang Xu:
Noise correction to improve data and model quality for crowdsourcing. Eng. Appl. Artif. Intell. 82: 184-191 (2019) - [j41]Wenqiang Xu, Liangxiao Jiang
, Liangjun Yu:
An attribute value frequency-based instance weighting filter for naive Bayes. J. Exp. Theor. Artif. Intell. 31(2): 225-236 (2019) - [j40]Liangxiao Jiang
, Chaoqun Li:
Two improved attribute weighting schemes for value difference metric. Knowl. Inf. Syst. 60(2): 949-970 (2019) - [j39]Lungan Zhang, Liangxiao Jiang
, Chaoqun Li:
A discriminative model selection approach and its application to text classification. Neural Comput. Appl. 31(4): 1173-1187 (2019) - [j38]Liangjun Yu, Liangxiao Jiang
, Dianhong Wang, Lungan Zhang:
Toward naive Bayes with attribute value weighting. Neural Comput. Appl. 31(10): 5699-5713 (2019) - [j37]Liangxiao Jiang
, Lungan Zhang, Liangjun Yu, Dianhong Wang:
Class-specific attribute weighted naive Bayes. Pattern Recognit. 88: 321-330 (2019) - [j36]Liangxiao Jiang
, Lungan Zhang, Chaoqun Li, Jia Wu
:
A Correlation-Based Feature Weighting Filter for Naive Bayes. IEEE Trans. Knowl. Data Eng. 31(2): 201-213 (2019) - [c32]Hao Zhang, Liangxiao Jiang, Wenqiang Xu:
Multiple Noisy Label Distribution Propagation for Crowdsourcing. IJCAI 2019: 1473-1479 - 2018
- [c31]Liangjun Yu, Liangxiao Jiang, Lungan Zhang, Dianhong Wang:
Weight Adjusted Naive Bayes. ICTAI 2018: 825-831 - [c30]Chen Qiu, Liangxiao Jiang
, Zhihua Cai:
Using Differential Evolution to Estimate Labeler Quality for Crowdsourcing. PRICAI 2018: 165-173 - [c29]Hao Zhang, Liangxiao Jiang
, Wenqiang Xu:
Differential Evolution-Based Weighted Majority Voting for Crowdsourcing. PRICAI 2018: 228-236 - 2017
- [j35]Chen Qiu, Liangxiao Jiang
, Chaoqun Li:
Randomly selected decision tree for test-cost sensitive learning. Appl. Soft Comput. 53: 27-33 (2017) - [j34]Liangjun Yu, Liangxiao Jiang
, Dianhong Wang, Lungan Zhang:
Attribute Value Weighted Average of One-Dependence Estimators. Entropy 19(9): 501 (2017) - [j33]Chaoqun Li, Liangxiao Jiang
, Hongwei Li, Jia Wu
, Peng Zhang:
Toward value difference metric with attribute weighting. Knowl. Inf. Syst. 50(3): 795-825 (2017) - 2016
- [j32]Liangxiao Jiang
, Chaoqun Li, Shasha Wang, Lungan Zhang:
Deep feature weighting for naive Bayes and its application to text classification. Eng. Appl. Artif. Intell. 52: 26-39 (2016) - [j31]Lungan Zhang, Liangxiao Jiang
, Chaoqun Li:
A New Feature Selection Approach to Naive Bayes Text Classifiers. Int. J. Pattern Recognit. Artif. Intell. 30(2): 1650003:1-1650003:17 (2016) - [j30]Liangxiao Jiang
, Shasha Wang, Chaoqun Li, Lungan Zhang:
Structure extended multinomial naive Bayes. Inf. Sci. 329: 346-356 (2016) - [j29]Lungan Zhang, Liangxiao Jiang
, Chaoqun Li, Ganggang Kong:
Two feature weighting approaches for naive Bayes text classifiers. Knowl. Based Syst. 100: 137-144 (2016) - [j28]Chaoqun Li, Victor S. Sheng, Liangxiao Jiang
, Hongwei Li:
Noise filtering to improve data and model quality for crowdsourcing. Knowl. Based Syst. 107: 96-103 (2016) - [j27]Ganggang Kong, Liangxiao Jiang
, Chaoqun Li:
Beyond accuracy: Learning selective Bayesian classifiers with minimal test cost. Pattern Recognit. Lett. 80: 165-171 (2016) - [c28]Lungan Zhang, Liangxiao Jiang
, Chaoqun Li:
C4.5 or Naive Bayes: A Discriminative Model Selection Approach. ICANN (1) 2016: 419-426 - 2015
- [j26]Chen Qiu, Liangxiao Jiang
, Chaoqun Li:
Not always simple classification: Learning SuperParent for class probability estimation. Expert Syst. Appl. 42(13): 5433-5440 (2015) - [j25]Liangxiao Jiang
, Chen Qiu, Chaoqun Li:
A Novel Minority Cloning Technique for Cost-Sensitive Learning. Int. J. Pattern Recognit. Artif. Intell. 29(4): 1551004:1-1551004:18 (2015) - [j24]Shasha Wang, Liangxiao Jiang
, Chaoqun Li:
Adapting naive Bayes tree for text classification. Knowl. Inf. Syst. 44(1): 77-89 (2015) - [c27]Chen Qiu, Liangxiao Jiang
, Ganggang Kong:
A differential evolution-based method for class-imbalanced cost-sensitive learning. IJCNN 2015: 1-8 - 2014
- [j23]Chaoqun Li, Liangxiao Jiang
, Hongwei Li:
Naive Bayes for value difference metric. Frontiers Comput. Sci. 8(2): 255-264 (2014) - [j22]Liangxiao Jiang
, Chaoqun Li, Harry Zhang, Zhihua Cai:
A Novel Distance Function: frequency difference Metric. Int. J. Pattern Recognit. Artif. Intell. 28(2) (2014) - [j21]Liangxiao Jiang
, Zhihua Cai, Dianhong Wang, Harry Zhang:
Bayesian Citation-KNN with distance weighting. Int. J. Mach. Learn. Cybern. 5(2): 193-199 (2014) - [j20]Liangxiao Jiang
, Chaoqun Li, Shasha Wang:
Cost-sensitive Bayesian network classifiers. Pattern Recognit. Lett. 45: 211-216 (2014) - [j19]Chaoqun Li, Liangxiao Jiang
, Hongwei Li:
Local value difference metric. Pattern Recognit. Lett. 49: 62-68 (2014) - [c26]Shasha Wang, Liangxiao Jiang, Chaoqun Li:
A CFS-Based Feature Weighting Approach to Naive Bayes Text Classifiers. ICANN 2014: 555-562 - 2013
- [j18]Liangxiao Jiang
, Zhihua Cai, Harry Zhang, Dianhong Wang:
Naive Bayes text classifiers: a locally weighted learning approach. J. Exp. Theor. Artif. Intell. 25(2): 273-286 (2013) - [j17]Guiling Li, Olli Bräysy, Liangxiao Jiang
, Zongda Wu, Yuanzhen Wang:
Finding time series discord based on bit representation clustering. Knowl. Based Syst. 54: 243-254 (2013) - [j16]Liangxiao Jiang
, Chaoqun Li:
An Augmented Value Difference Measure. Pattern Recognit. Lett. 34(10): 1169-1174 (2013) - [c25]Liangxiao Jiang
, Chaoqun Li, Zhihua Cai, Harry Zhang:
Sampled Bayesian Network Classifiers for Class-Imbalance and Cost-Sensitive Learning. ICTAI 2013: 512-517 - [c24]Chaoqun Li, Liangxiao Jiang
, Hongwei Li, Shasha Wang:
Attribute Weighted Value Difference Metric. ICTAI 2013: 575-580 - 2012
- [j15]Liangxiao Jiang
, Zhihua Cai, Harry Zhang, Dianhong Wang:
Not so greedy: Randomly Selected Naive Bayes. Expert Syst. Appl. 39(12): 11022-11028 (2012) - [j14]Liangxiao Jiang
, Dianhong Wang, Zhihua Cai:
Discriminatively Weighted Naive Bayes and its Application in Text Classification. Int. J. Artif. Intell. Tools 21(1) (2012) - [j13]Dianhong Wang, Xingwen Liu, Liangxiao Jiang, Xiaoting Zhang, Yongguang Zhao
:
Rough Set Approach to Multivariate Decision Trees Inducing. J. Comput. 7(4): 870-879 (2012) - [j12]Liangxiao Jiang
, Harry Zhang, Zhihua Cai, Dianhong Wang:
Weighted average of one-dependence estimators†. J. Exp. Theor. Artif. Intell. 24(2): 219-230 (2012) - [j11]Liangxiao Jiang
:
Learning Instance Weighted Naive Bayes from labeled and unlabeled data. J. Intell. Inf. Syst. 38(1): 257-268 (2012) - [j10]Liangxiao Jiang
, Zhihua Cai, Dianhong Wang, Harry Zhang:
Improving Tree augmented Naive Bayes for class probability estimation. Knowl. Based Syst. 26: 239-245 (2012) - 2011
- [j9]Liangxiao Jiang
:
Learning random forests for ranking. Frontiers Comput. Sci. China 5(1): 79-86 (2011) - [j8]Liangxiao Jiang, Chaoqun Li:
Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination. J. Comput. 6(7): 1325-1331 (2011) - [j7]Liangxiao Jiang, Chaoqun Li:
An Empirical Study on Class Probability Estimates in Decision Tree Learning. J. Softw. 6(7): 1368-1373 (2011) - [j6]Liangxiao Jiang
:
Random one-dependence estimators. Pattern Recognit. Lett. 32(3): 532-539 (2011) - [c23]Xingwen Liu, Dianhong Wang, Liangxiao Jiang, Fenxiong Chen, Shengfeng Gan:
A novel method for inducing ID3 decision trees based on variable precision rough set. ICNC 2011: 494-497
2000 – 2009
- 2009
- [j5]Liangxiao Jiang
, Chaoqun Li, Zhihua Cai:
Decision Tree with Better Class Probability Estimation. Int. J. Pattern Recognit. Artif. Intell. 23(4): 745-763 (2009) - [j4]Liangxiao Jiang
, Chaoqun Li, Zhihua Cai:
Learning decision tree for ranking. Knowl. Inf. Syst. 20(1): 123-135 (2009) - [j3]Liangxiao Jiang
, Harry Zhang, Zhihua Cai:
A Novel Bayes Model: Hidden Naive Bayes. IEEE Trans. Knowl. Data Eng. 21(10): 1361-1371 (2009) - 2008
- [j2]Wenyin Gong, Zhihua Cai, Liangxiao Jiang
:
Enhancing the performance of differential evolution using orthogonal design method. Appl. Math. Comput. 206(1): 56-69 (2008) - [j1]Liangxiao Jiang
, Dianhong Wang, Harry Zhang, Zhihua Cai, Bo Huang:
Using Instance cloning to Improve Naive Bayes for Ranking. Int. J. Pattern Recognit. Artif. Intell. 22(6): 1121-1140 (2008) - [c22]Liangxiao Jiang
, Chaoqun Li, Jia Wu
, Jian Zhu:
A Combined Classification Algorithm Based on C4.5 and NB. ISICA 2008: 350-359 - 2007
- [c21]Liangxiao Jiang
, Dianhong Wang, Zhihua Cai, Xuesong Yan:
Survey of Improving Naive Bayes for Classification. ADMA 2007: 134-145 - [c20]Liangxiao Jiang
, Harry Zhang, Dianhong Wang, Zhihua Cai:
Learning Locally Weighted C4.4 for Class Probability Estimation. Discovery Science 2007: 104-115 - [c19]Dianhong Wang, Liangxiao Jiang
:
An Improved Attribute Selection Measure for Decision Tree Induction. FSKD (4) 2007: 654-658 - [c18]Liangxiao Jiang
, Zhihua Cai, Dianhong Wang, Siwei Jiang:
Survey of Improving K-Nearest-Neighbor for Classification. FSKD (1) 2007: 679-683 - [c17]Zhihua Cai, Dianhong Wang, Liangxiao Jiang
:
K-Distributions: A New Algorithm for Clustering Categorical Data. ICIC (2) 2007: 436-443 - [c16]Liangxiao Jiang
, Dianhong Wang, Zhihua Cai:
Scaling Up the Accuracy of Bayesian Network Classifiers by M-Estimate. ICIC (2) 2007: 475-484 - 2006
- [c15]Liangxiao Jiang
, Harry Zhang:
Learning Naive Bayes for Probability Estimation by Feature Selection. Canadian AI 2006: 503-514 - [c14]Liangxiao Jiang
, Harry Zhang:
Lazy Averaged One-Dependence Estimators. Canadian AI 2006: 515-525 - [c13]Liangxiao Jiang
, Harry Zhang, Zhihua Cai:
Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted. FSKD 2006: 365-368 - [c12]Dan Zeng, Sifa Zhang, Zhihua Cai, Siwei Jiang, Liangxiao Jiang
:
Augmented Naive Bayes Based on Evolutional Strategy. ISDA (1) 2006: 446-450 - [c11]Dan Zeng, Sifa Zhang, Zhihua Cai, Siwei Jiang, Liangxiao Jiang
:
A Novel One-dependence Estimator Based on Multi-parents. ISDA (1) 2006: 639-643 - [c10]Chaoqun Li, Liangxiao Jiang:
Using Locally Weighted Learning to Improve SMOreg for Regression. PRICAI 2006: 375-384 - [c9]Liangxiao Jiang
, Harry Zhang:
Weightily Averaged One-Dependence Estimators. PRICAI 2006: 970-974 - 2005
- [c8]Harry Zhang, Liangxiao Jiang, Jiang Su:
Hidden Naive Bayes. AAAI 2005: 919-924 - [c7]Liangxiao Jiang
, Harry Zhang, Jiang Su:
Learning k-Nearest Neighbor Naive Bayes for Ranking. ADMA 2005: 175-185 - [c6]Liangxiao Jiang
, Harry Zhang, Zhihua Cai, Jiang Su:
One Dependence Augmented Naive Bayes. ADMA 2005: 186-194 - [c5]Liangxiao Jiang
, Harry Zhang, Jiang Su:
Instance Cloning Local Naive Bayes. Canadian AI 2005: 280-291 - [c4]Liangxiao Jiang
, Harry Zhang, Zhihua Cai, Jiang Su:
Learning Tree Augmented Naive Bayes for Ranking. DASFAA 2005: 688-698 - [c3]Liangxiao Jiang
, Harry Zhang:
Learning Instance Greedily Cloning Naive Bayes for Ranking. ICDM 2005: 202-209 - [c2]Harry Zhang, Liangxiao Jiang, Jiang Su:
Augmenting naive Bayes for ranking. ICML 2005: 1020-1027 - [c1]Liangxiao Jiang
, Yuanyuan Guo:
Learning Lazy Naive Bayesian Classifiers for Ranking. ICTAI 2005: 412-416
Coauthor Index
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