Liangxiao Jiang is currently a full professor at the School of Computer Science, China University of Geosciences, Wuhan, China. His main research interests include data mining and machine learning. In these domains, he has published more than 150 papers in renowned international journals and conferences such as TPAMI, TKDE, TSC, TNNLS, TKDD, TSMC, TBD, TETCI, SCIS, FCS, PR, INS, IS, KAIS, IJAR, AAAI, IJCAI, ICML, NIPS, UAI, ICDM, and DASFAA. He was selected as World's Top 2% Scientists (2020-2025) and Elsevier Highly Cited Chinese Researchers (2020-2024). He serves/ed as AE/AC/SPC/PC members of several renowned international journals and conferences such as EAAI, UAI, AAAI, IJCAI, ICML, and NIPS.

Education:

2006/09-2009/06, Ph.D. student at China University of Geosciences

2001/09-2004/06, M.Sc. student at China University of Geosciences

1997/09-2001/06, B.Sc. student at China University of Geosciences

Career:

2011/12-Present,  Professor at China University of Geosciences

2009/07-2011/11, Associate Professor at China University of Geosciences

2006/07-2009/06, Lecturer at China University of Geosciences

2004/07-2006/06, Assistant at China University of Geosciences

Interest:

Data Mining Tasks: classification, ranking, class probability estimation, feature selection, distance measure, defect detection

Machine Learning Techniques: crowdsourcing learning, Bayesian learning, cost-sensitive learning, class-imbalance learning

Course:

Machine Learning (For Undergraduate Students)

Frontiers of Data Mining and Machine Learning (For Graduate Students)

Paper: (*Corresponding authors) 

Q. Ji, L. Jiang*, W. Zhang, and C. Li. Learning from Crowds by Class-specific Instance Weighting. IEEE Transactions on Emerging Topics in Computational Intelligence, doi: 10.1109/TETCI.2025.3551982.

B. Su, L. Jiang*, and S. Si. Confident Learning-based Noise Correction for Crowdsourcing. Pattern Recognition, 2026, 169: 111962.

C. Pan, L. Jiang*, and S. Si. Cross-Worker Joint Modeling-based Label Integration for Crowdsourcing. International Journal of Approximate Reasoning, 2025, 187: 109570.

C. Pan, L. Jiang*, and C. Li. Three-way Decision-based Label Integration for Crowdsourcing. Pattern Recognition, 2025, 158: 111034.

C. Li, L. Jiang*, W. Zhang, L. Yu, and H. Zhang. Instance Correlation Graph-based Naive Bayes. In: Proceedings of the 42nd International Conference on Machine Learning, ICML 2025, PMLR 267: 35021-35033. (CCF-A)

W. Zhang, L. Jiang*, and C. Li. TLLC: Transfer Learning-based Label Completion for Crowdsourcing. In: Proceedings of the 42nd International Conference on Machine Learning, ICML 2025, PMLR 267: 75178-75191. (CCF-A)

T. Wu, L. Jiang*, W. Zhang, and C. Li. Label Distribution Propagation-based Label Completion for Crowdsourcing. In: Proceedings of the 42nd International Conference on Machine Learning, ICML 2025, PMLR 267: 67369-67381. (CCF-A)

J. Li, L. Jiang*, and W. Zhang. Label Consistency-based Ground Truth Inference for Crowdsourcing. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(5): 9408-9421. (CAAI-A)

X. Wu, L. Jiang*, W. Zhang, and C. Li. Worker Similarity-based Label Completion for Crowdsourcing. IEEE Transactions on Big Data, 2025, 11(2): 710-721.

H. Zhang, L. Jiang*, W. Zhang, and G. I. Webb. Dual-View Learning from Crowds. ACM Transactions on Knowledge Discovery from Data, 2025, 19(3): 61.

W. Zhang, L. Jiang*, and C. Li. ELDP: Enhanced Label Distribution Propagation for Crowdsourcing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(3): 1850-1862. (CCF-A)

W. Zhang, L. Jiang*, and C. Li. KFNN: K-Free Nearest Neighbor For Crowdsourcing. In: Proceedings of the 38th Annual Conference on Neural Information Processing Systems, NIPS 2024, Advances in Neural Information Processing Systems 37: 116493-116512. (CCF-A)

W. Zhang, L. Jiang*, and C. Li. IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing. In: Proceedings of the 38th Annual Conference on Neural Information Processing Systems, NIPS 2024, Advances in Neural Information Processing Systems 37: 85722-85741. (CCF-A)

B. Yang, L. Jiang*, and W. Zhang. Probabilistic Matrix Factorization-based Three-stage Label Completion for Crowdsourcing. In: Proceedings of the 24th IEEE International Conference on Data Mining, ICDM 2024, pp. 540-549.

J. Li, L. Jiang*, X. Wu, and W. Zhang. Learning from Crowds with Dual-View K-Nearest Neighbor. In: Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence, UAI 2024, PMLR 244: 2238-2249. (CAAI-A)

J. Li, L. Jiang*, C. Li, and W. Zhang. Label Consistency-based Worker Filtering for Crowdsourcing. In: Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence, UAI 2024, PMLR 244: 2226-2237. (CAAI-A)

Z. Chen, L. Jiang*, W. Zhang, and C. Li. Weighted Adversarial Learning from Crowds. IEEE Transactions on Services Computing, 2024, 17(6): 4467-4480. (CCF-A)

W. Zhang, L. Jiang*, Z. Chen, and C. Li. FNNWV: Farthest-Nearest Neighbor-based Weighted Voting for Class-Imbalanced Crowdsourcing. Science China Information Sciences, 2024, 67(10): 202102. (CCF-A)

H. Li, L. Jiang*, and C. Li. Certainty Weighted Voting-based Noise Correction for Crowdsourcing. Pattern Recognition, 2024, 150: 110325.

Y. Hu, L. Jiang*, and W. Zhang. Worker Similarity-based Noise Correction for Crowdsourcing. Information Systems, 2024, 121: 102321.

L. Ren, L. Jiang*, W. Zhang, and C. Li. Label Distribution Similarity-based Noise Correction for Crowdsourcing. Frontiers of Computer Science, 2024, 18(5): 185323.

Y. Zhang, L. Jiang*, and C. Li*. Instance Redistribution-based Label Integration for Crowdsourcing. Information Sciences, 2024, 674: 120702.

K. Zhu, S. Xue*, and L. Jiang*. Improving Label Quality in Crowdsourcing using Deep Co-Teaching-based Noise Correction. International Journal of Machine Learning and Cybernetics, 2023, 14(10): 3641-3654.

X. Wu, L. Jiang*, W. Zhang, and C. Li. Three-way Decision-based Noise Correction for Crowdsourcing. International Journal of Approximate Reasoning, 2023, 160: 108973.

Q. Ji, L. Jiang*, and W. Zhang. Instance Weighting-based Noise Correction for Crowdsourcing. In: Proceedings of the 19th International Conference on Intelligent Computing, ICIC 2023, LNAI 14089: 285–297.

Q. Ji, L. Jiang*, and W. Zhang. Dual-View Noise Correction for Crowdsourcing. IEEE Internet of Things Journal, 2023, 10(13): 11804-11812.

H. Li, L. Jiang*, and S. Xue. Neighborhood Weighted Voting-based Noise Correction for Crowdsourcing. ACM Transactions on Knowledge Discovery from Data, 2023, 17(7): 96.

H. Zhang, L. Jiang*, W. Zhang, and C. Li. Multi-view Attribute Weighted Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(7): 7291-7302. (CCF-A)

H. Zhang, L. Jiang*, and G. I. Webb*. Rigorous Non-Disjoint Discretization for Naive Bayes. Pattern Recognition, 2023, 140: 109554.

Y. Zhang, L. Jiang*, and C. Li. Attribute Augmentation-based Label Integration for Crowdsourcing. Frontiers of Computer Science, 2023, 17(5): 175331.

L. Ren, L. Jiang*, and C. Li*. Label Confidence-based Noise Correction for Crowdsourcing. Engineering Applications of Artificial Intelligence, 2023, 117: 105624.

Y. Hu, L. Jiang*, and C. Li*. Instance Difficulty-based Noise Correction for Crowdsourcing. Expert Systems with Applications, 2023, 212: 118794.

X. Li, C. Li*, and L. Jiang. A Multi-View-Based Noise Correction Algorithm for Crowdsourcing Learning. Information Fusion, 2023, 91: 529-541.

W. Li, C. Li*, and L. Jiang*. Learning from Crowds with Robust Logistic Regression. Information Sciences, 2023, 639: 119010.

W. Yang, C. Li*, and L. Jiang. Learning from Crowds with Robust Support Vector Machines. Science China Information Sciences, 2023, 66(3): 132103. (CCF-A)

W. Yang, C. Li*, and L. Jiang. Learning from Crowds with Decision Trees. Knowledge and Information Systems, 2022, 64(8): 2123-2140.

H. Zhang, L. Jiang*, and C. Li. Attribute Augmented and Weighted Naive Bayes. Science China Information Sciences, 2022, 65(12): 222101. (CCF-A)

L. Jiang*, H. Zhang, F. Tao, and C. Li. Learning from Crowds with Multiple Noisy Label Distribution Propagation. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(11): 6558-6568. (CAAI-A)

Z. Chen, L. Jiang*, and C. Li. Label Distribution-based Noise Correction for Multiclass Crowdsourcing. International Journal of Intelligent Systems, 2022, 37(9): 5752-5767.

Z. Chen, L. Jiang*, and C. Li*. Label Augmented and Weighted Majority Voting for Crowdsourcing. Information Sciences, 2022, 606: 397-409.

Y. Dong, L. Jiang*, and C. Li. Improving Data and Model Quality in Crowdsourcing using Co-Training-based Noise Correction. Information Sciences, 2022, 583: 174-188.

H. Zhang and L. Jiang*. Fine Tuning Attribute Weighted Naive Bayes. Neurocomputing, 2022, 488: 402-411. 

H. Zhang, L. Jiang*, and C. Li. Collaboratively Weighted Naive Bayes. Knowledge and Information Systems, 2021, 63(12): 3159-3182. 

W. Xu, L. Jiang*, and C. Li. Resampling-based Noise Correction for Crowdsourcing. Journal of Experimental & Theoretical Artificial Intelligence, 2021, 33(6): 985-999.

F. Tao, L. Jiang*, and C. Li. Differential Evolution-based Weighted Soft Majority Voting for Crowdsourcing. Engineering Applications of Artificial Intelligence, 2021, 106: 104474.

H. Zhang, L. Jiang*, and C. Li*.CS-ResNet: Cost-Sensitive Residual Convolutional Neural Network for PCB Cosmetic Defect Detection. Expert Systems with Applications, 2021, 185: 115673. 

H. Zhang, L. Jiang*, and L. Yu. Attribute and Instance Weighted Naive Bayes. Pattern Recognition, 2021, 111: 107674. 

L. Chen, L. Jiang*, and C. Li*. Using Modified Term Frequency to Improve Term Weighting for Text Classification. Engineering Applications of Artificial Intelligence, 2021, 101: 104215.

L. Chen, L. Jiang*, and C. Li*. Modified DFS-based Term Weighting Scheme for Text Classification. Expert Systems with Applications, 2021, 168: 114438.

L. Jiang*, G. Kong, and C. Li. Wrapper Framework for Test-Cost-Sensitive Feature Selection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(3): 1747-1756. 

W. Xu, L. Jiang*, and C. Li. Improving Data and Model Quality in Crowdsourcing Using Cross-Entropy-based Noise Correction. Information Sciences, 2021, 546: 803-814. 

F. Gong, X. Wang*, L. Jiang*, S. M. Rahimi, and D. Wang. Fine-grained Attribute Weighted Inverted Specific-Class Distance Measure for Nominal Attributes. Information Sciences, 2021, 578: 848-869.

F. Gong, L. Jiang*, H. Zhang, D. Wang, and X. Guo. Gain Ratio Weighted Inverted Specific-Class Distance Measure for Nominal Attributes. International Journal of Machine Learning and Cybernetics, 2020, 11(10): 2237-2246.

F. Gong, L. Jiang*, D. Wang, and X. Guo. Averaged One-Dependence Inverted Specific-Class Distance Measure for Nominal Attributes. Journal of Experimental & Theoretical Artificial Intelligence, 2020, 32(4): 651-663.

F. Tao, L. Jiang* and C. Li. Label Similarity-based Weighted Soft Majority Voting and Pairing for Crowdsourcing. Knowledge and Information Systems, 2020, 62(7): 2521-2538.

H. Zhang, L. Jiang*, and L. Yu. Class-specific Attribute Value Weighting for Naive Bayes. Information Sciences, 2020, 508: 260-274.

L. Yu, L. Jiang*, D. Wang, and L. Zhang. Toward Naive Bayes with Attribute Value Weighting. Neural Computing & Applications, 2019, 31(10): 5699-5713. 

L. Zhang, L. Jiang*, and C. Li. A Discriminative Model Selection Approach and Its Application to Text Classification. Neural Computing & Applications, 2019, 31(4): 1173-1187.

C. Li*, L. Jiang, and W. Xu. Noise Correction to Improve Data and Model Quality for Crowdsourcing. Engineering Applications of Artificial Intelligence, 2019, 82: 184-191. 

W. Xu, L. Jiang*, and L. Yu. An Attribute Value Frequency-based Instance Weighting Filter for Naive Bayes. Journal of Experimental & Theoretical Artificial Intelligence, 2019, 31(2): 225-236.

L. Jiang* and C. Li. Two Improved Attribute Weighting Schemes for Value Difference Metric. Knowledge and Information Systems, 2019, 60(2): 949-970. 

L. Jiang*, L. Zhang, L. Yu, and D. Wang. Class-specific Attribute Weighted Naive Bayes. Pattern Recognition, 2019, 88: 321-330.

L. Jiang*, L. Zhang, C. Li, and J. Wu. A Correlation-based Feature Weighting Filter for Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 201-213. (CCF-A)

H. Zhang, L. Jiang*, and W. Xu. Multiple Noisy Label Distribution Propagation for Crowdsourcing. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 1473-1479. (CCF-A)

L. Yu, L. Jiang*, L. Zhang, and D. Wang. Weight Adjusted Naive Bayes. In: Proceedings of the 30th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2018, pp. 825-831.

H. Zhang, L. Jiang*, and W. Xu. Differential Evolution-Based Weighted Majority Voting for Crowdsourcing. In: Proceedings of the 15th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018, LNAI 11013: 228-236.

C. Qiu, L. Jiang*, and Z. Cai. Using Differential Evolution to Estimate Labeler Quality for Crowdsourcing. In: Proceedings of the 15th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018, LNAI 11013: 165-173.

C. Qiu, L. Jiang*, and C. Li. Randomly Selected Decision Tree for Test-Cost Sensitive Learning. Applied Soft Computing, 2017, 53: 27-33.

C. Li, L. Jiang*, H. Li, J. Wu, and P. Zhang. Toward Value Difference Metric with Attribute Weighting. Knowledge and Information Systems, 2017, 50(3): 795-825.

C. Li, S. Sheng, L. Jiang*, and H. Li*. Noise Filtering to Improve Data and Model Quality for Crowdsourcing. Knowledge-Based Systems, 2016, 107: 96-103.

G. Kong, L. Jiang*, and C. Li*. Beyond Accuracy: Learning Selective Bayesian Classifiers with Minimal Test Cost. Pattern Recognition Letters, 2016, 80: 165-171.

L. Zhang, L. Jiang*, C. Li*, and G. Kong. Two Feature Weighting Approaches for Naive Bayes Text Classifiers. Knowledge-Based Systems, 2016, 100: 137-144.

L. Zhang, L. Jiang*, and C. Li. A New Feature Selection Approach to Naive Bayes Text Classifiers. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(2): 1650003.

L. Zhang, L. Jiang*, and C. Li. C4.5 or Naive Bayes: A Discriminative Model Selection Approach. In: Proceedings of the 25th International Conference on Artificial Neural Networks, ICANN 2016, LNCS 9886: 419-426.

L. Jiang*, C. Li*, S. Wang, and L. Zhang. Deep Feature Weighting for Naive Bayes and Its Application to Text Classification. Engineering Applications of Artificial Intelligence, 2016, 52: 26-39.

L. Jiang*, S. Wang, C. Li, and L. Zhang. Structure Extended Multinomial Naive Bayes. Information Sciences, 2016, 329: 346-356.

C. Qiu, L. Jiang*, and G. Kong. A Differential Evolution-Based Method for Class-Imbalanced Cost-Sensitive Learning. In: Proceedings of the 2015 International Joint Conference on Neural Networks, IJCNN 2015, pp. 1-8.

C. Qiu, L. Jiang*, and C. Li. Not always simple classification: Learning SuperParent for Class Probability Estimation. Expert Systems with Applications, 2015, 42(13): 5433-5440.

L. Jiang*, C. Qiu, and C. Li. A Novel Minority Cloning Technique for Cost-Sensitive Learning. International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29(4): 1551004.

S. Wang, L. Jiang*, and C. Li. Adapting Naive Bayes Tree for Text Classification. Knowledge and Information Systems, 2015, 44(1): 77-89.

S. Wang, L. Jiang*, and C. Li. A CFS-based Feature Weighting Approach to Naive Bayes Text Classifiers. In: Proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, LNCS 8681: 555-562.

L. Jiang*, C. Li, and S. Wang. Cost-Sensitive Bayesian Network Classifiers. Pattern Recognition Letters, 2014, 45: 211-216.

L. Jiang*, C. Li, H. Zhang, and Z. Cai. A Novel Distance Function: Frequency Difference Metric. International Journal of Pattern Recognition and Artificial Intelligence, 2014, 28(2): 1451002.

L. Jiang*, Z. Cai, D. Wang, and H. Zhang. Bayesian Citation-KNN with Distance Weighting. International Journal of Machine Learning and Cybernetics, 2014, 5(2): 193-199.

C. Li*, L. Jiang, and H. Li. Naive Bayes for Value Difference Metric. Frontiers of Computer Science, 2014, 8(2): 255-264. 

C. Li*, L. Jiang, and H. Li. Local Value Difference Metric. Pattern Recognition Letters, 2014, 49: 62-68. 

C. Li*, L. Jiang, H. Li, and S. Wang. Attribute Weighted Value Difference Metric. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013, pp. 575-580. 

L. Jiang*, C. Li, Z. Cai, and H. Zhang. Sampled Bayesian Network Classifiers for Class-Imbalanced and Cost-Sensitive Learning. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013, pp. 512-517.

L. Jiang* and C. Li. An Augmented Value Difference Measure. Pattern Recognition Letters, 2013, 34(10): 1169-1174.

L. Jiang, Z. Cai*, H. Zhang, and D. Wang. Naive Bayes Text Classifiers: A Locally Weighted Learning Approach. Journal of Experimental & Theoretical Artificial Intelligence, 2013, 25(2): 273-286. 

L. Jiang*, Z. Cai*, H. Zhang, and D. Wang. Not so greedy: Randomly Selected Naive Bayes. Expert Systems with Applications, 2012, 39(12): 11022-11028.

L. Jiang*, H. Zhang, Z. Cai, and D. Wang. Weighted Average of One-Dependence Estimators. Journal of Experimental & Theoretical Artificial Intelligence, 2012, 24(2): 219-230. 

L. Jiang*, D. Wang, and Z. Cai. Discriminatively Weighted Naive Bayes and Its Application in Text Classification. International Journal on Artificial Intelligence Tools, 2012, 21(1): 1250007.

L. Jiang, Z. Cai*, D. Wang, and H. Zhang. Improving Tree Augmented Naive Bayes for Class Probability Estimation. Knowledge-Based Systems, 2012, 26: 239-245. 

L. Jiang*. Learning Instance Weighted Naive Bayes from Labeled and Unlabeled Data. Journal of Intelligent Information Systems, 2012, 38(1): 257-268.

L. Jiang*. Learning Random Forests for Ranking. Frontiers of Computer Science in China, 2011, 5(1): 79-86.

L. Jiang*. Random One-Dependence Estimators. Pattern Recognition Letters, 2011, 32(3): 532-539. 

L. Jiang*, Z. Cai, and D. Wang. Improving Naive Bayes for Classification. International Journal of Computers and Applications, 2010, 32(3): 328-332.

L. Jiang*, H. Zhang, and Z. Cai. A Novel Bayes Model: Hidden Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(10): 1361-1371. (CCF-A)

L. Jiang*, C. Li, and Z. Cai. Learning Decision Tree for Ranking. Knowledge and Information Systems, 2009, 20(1): 123-135.

L. Jiang*, C. Li, and Z. Cai. Decision Tree with Better Class Probability Estimation. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23(4): 745-763.

L. Jiang*, D. Wang, Z. Cai, S. Jiang, and X. Yan. Scaling Up the Accuracy of K-Nearest-Neighbor Classifiers: A Naive-Bayes Hybrid. International Journal of Computers and Applications, 2009, 31(1): 36-43. 

L. Jiang*, D. Wang, H. Zhang, Z. Cai, and B. Huang. Using Instance Cloning to Improve Naive Bayes for Ranking. International Journal of Pattern Recognition and Artificial Intelligence, 2008, 22(6): 1121-1140. 

L. Jiang*, C. Li, J. Wu, and J. Zhu. A Combined Classification Algorithm Based on C4.5 and NB. In: Proceedings of the 3rd International Symposium on Intelligence Computation and Applications, ISICA 2008, LNCS 5370: 350-359.

L. Jiang*, H. Zhang, D. Wang, and Z. Cai. Learning Locally Weighted C4.4 for Class Probability Estimation. In: Proceedings of the 10th International Conference on Discovery Science, DS 2007, LNAI 4755: 104-115.

D. Wang* and L. Jiang. An Improved Attribute Selection Measure for Decision Tree Induction. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Volume 4, pp. 654-658.

L. Jiang*, Z. Cai, D. Wang, and S. Jiang. Survey of Improving K-Nearest-Neighbor for Classification. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Volume 1, pp. 679-683.

L. Jiang*, D. Wang, and Z. Cai. Scaling Up the Accuracy of Bayesian Network Classifiers by M-Estimate. In: Proceedings of the 3rd International Conference on Intelligent Computing, ICIC 2007, LNAI 4682: 475-484.

Z. Cai*, D. Wang, and L. Jiang. K-Distributions: A New Algorithm for Clustering Categorical Data. In: Proceedings of the 3rd International Conference on Intelligent Computing, ICIC 2007, LNAI 4682: 436-443.

L. Jiang*, D. Wang, Z. Cai, and X. Yan. Survey of Improving Naive Bayes for Classification. In: Proceedings of the 3rd International Conference on Advanced Data Mining and Applications, ADMA 2007, LNAI 4632: 134-145.

L. Jiang*, H. Zhang, and Z. Cai. Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted. In: Proceedings of the 3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006, LNAI 4223: 365-368.

L. Jiang* and H. Zhang. Weightily Averaged One-Dependence Estimators. In: Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, LNAI 4099: 970-974. 

L. Jiang* and H. Zhang. Lazy Averaged One-Dependence Estimators. In: Proceedings of the 19th Canadian Conference on Artificial Intelligence, CAI 2006, LNAI 4013: 515-525.

L. Jiang* and H. Zhang. Learning Naive Bayes for Probability Estimation by Feature Selection. In: Proceedings of the 19th Canadian Conference on Artificial Intelligence, CAI 2006, LNAI 4013: 503-514. 

L. Jiang* and H. Zhang. Learning Instance Greedily Cloning Naive Bayes for Ranking. In: Proceedings of the 5th IEEE International Conference on Data Mining, ICDM 2005, pp. 202-209. 

L. Jiang* and Y. Guo. Learning Lazy Naive Bayesian Classifiers for Ranking. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2005, pp. 412-416.

H. Zhang*, L. Jiang, and J. Su. Augmenting Naive Bayes for Ranking. In: Proceedings of the 22nd International Conference on Machine Learning, ICML 2005, pp. 1020-1027. (CCF-A)

L. Jiang*, H. Zhang, and J. Su. Learning k-Nearest Neighbor Naive Bayes for Ranking. In: Proceedings of the 1st International Conference on Advanced Data Mining and Applications, ADMA 2005, LNAI 3584: 175-185.

L. Jiang*, H. Zhang, Z. Cai, and J. Su. One Dependence Augmented Naive Bayes. In: Proceedings of the 1st International Conference on Advanced Data Mining and Applications, ADMA 2005, LNAI 3584: 186-194. 

H. Zhang*, L. Jiang, and J. Su. Hidden Naive Bayes. In: Proceedings of the 20th National Conference on Artificial Intelligence, AAAI 2005, pp. 919-924. (CCF-A)

L. Jiang*, H. Zhang, and J. Su. Instance Cloning Local Naive Bayes. In: Proceedings of the 18th Canadian Conference on Artificial Intelligence, CAI 2005, LNAI 3501: 280-291.

L. Jiang*, H. Zhang, Z. Cai, and J. Su. Learning Tree Augmented Naive Bayes for Ranking. In: Proceedings of the 10th International Conference on Database Systems for Advanced Applications, DASFAA 2005, LNCS 3453: 688-698. 

L. Jiang*, H. Zhang, Z. Cai, and J. Su. Evolutional Naive Bayes. In: Proceedings of the 1st International Symposium on Intelligence Computation and Applications, ISICA 2005, pp. 344-350.

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Journal and Conference Rankings by CCF

Journal and Conference Rankings by CAAI

WEKA: Waikato Environment for Knowledge Analysis

CEKA: Crowd Environment and its Knowledge Analysis

KEEL: Knowledge Extraction based on Evolutionary Learning