[ DBLP ]

B: Book   (2)

C: Conference (83)

BC: Book Chapter (4)

J: Journal (39)

P: Patent (2)

ICDM(12);  IJCAI(5);  AAAI(5);  KDD(4);  CIKM (4);  ICDE(3);  MM(2);  ICML(1); CVPR(1)

KAIS(5); TKDE(3); TSMC(3); MMS(3); TMM(2); DMKD(2); DSS(2); TOIS(1); TCSV(1); AIR(1); PR(1); CI(1);  IS(1)

H-index as of July 2011 (at Google Scholar): 20

2011 and Beyond (6-J, 1-BC, and 9-C)

1.      X. Zhu, Cross Domain Semi-Supervised Learning Using Feature Formulation, IEEE Trans. On Systems Man and Cybernetics, Part B, accepted, to appear (J)

2.      Y. Zhang, X. Zhu, X. Wu, and J. P. Bond, Corrective Classification: Learning from Data Imperfections with Aggressive and Diverse Ensembling, Information Systems, accepted, to appear (J)

1.      X. Zhu, B. Li, X. Wu, D. He, and C. Zhang, CLAP: Collaborative Pattern Mining for Distributed Information Systems. Decision Support Systems, accepted, to appear (J).

2.      D. He, X. Zhu, and X. Wu, Mining Approximate Repeating Patterns from Sequence Data with Gap Constraints, Computational Intelligence, accepted, to appear (J)

3.      X. Zhu, W. Ding, P. Yu, and C. Zhang, One-Class Learning and Concept Summarization for Data Streams, Knowledge and Information Systems, special issue on Data Warehousing and Knowledge Discovery from Sensors and Streams (invited submission), accepted, to appear (J)

4.      D. Sun, L. Liu, P. Zhang, X. Zhu, and Y. Shi, Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model. International Journal of Data Warehousing and Mining, 7(3): 88-101, 2011 (J)

5.      M. Slavik, X. Zhu, I. Mahgoub, T. Khoshgoftaar, and R. Narayanan, Data Intensive Computing: A Biomedical Case Study in Gene Selection and Filtering, in Handbook of Data Intensive Computing, edited by Borko Furht and Armando Escalante, Springer, 2011. (BC)

6.      D. He, X. Zhu, and D. Parker, How Does Research Evolve? Pattern Mining for Research Meme Cycles, In Proc. of the 11th IEEE International Conference on Data Mining (ICDM-11), Dec. 11-14, 2011, Vancouver, Canada. (C)

7.      P. Zhang, B. Gao, X. Zhu, and L. Guo, Enabling Fast Lazy Learning for Data Streams. In Proc. of the 11th IEEE International Conference on Data Mining (ICDM-11), Dec. 11-14, 2011, Vancouver, Canada. (C)

8.      Z. Zhu, X. Zhu, Y. Ye, Y. Guo, and X. Xue, Transfer Active Learning, in Proc. Of the 20th ACM Conference on Information and Knowledge Management (CIKM-11), Glasgow, UK, Oct. 24-28, 2011. (C)

9.      Y. Fu, B. Li, X. Zhu, and C. Zhang, Do They Belong to the Same Class? Active Learning by Querying Pairwise Label Homogeneity, in Proc. Of the 20th ACM Conference on Information and Knowledge Management (CIKM-11), Glasgow, UK, Oct. 24-28, 2011. (C)

10.   P. Zhang, J. Li, P. Wang, B. Gao, X. Zhu, and L. Guo, Enabling Efficient Prediction for Ensemble Models on Data Streams, in Proc. of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Diego (KDD-11), CA, USA, August 21-24, 2011. (C)

11.   B. Li, X. Zhu, R. Li, C. Zhang, X. Xue, and X. Wu, Cross Domain Collaborative Filtering over Time. In Proc. of the 22nd Int’l Joint Conf. on Artificial Intelligence (IJCAI-11). Barcelona, Spain, July 16-22, 2011. (C)

12.   R. Li, B. Li, C. Jin, X. Xue, and X. Zhu, Tracking User-Preference Varying Speed in Collaborative Filtering. In Proc. of the 25th Conf. on Artificial Intelligence (AAAI-11). San Francisco, USA, August 7-11, 2011. (C)

13.   G. Liang, X. Zhu, and C. Zhang, An Empirical Study of Bagging Predictors for Different Learning Algorithms, Proc. of the 25th Annual American Association for the Advancement of Artificial Intelligence Conference (AAAI-11) [Student Poster], August 7-11, San Francisco, USA, 2011. (C)

14.   Y. Fu and X. Zhu, Optimal Subset Selection for Active Learning, Proc. of the 25th Annual American Association for the Advancement of Artificial Intelligence Conference (AAAI-11) [Student Poster], August 7-11, San Francisco, USA, 2011. (C)

15.   T. Guo, Z. Li, R. Guo, and X. Zhu, Large Scale Diagnosis using Associations between System Outputs and Components, Proc. of the 25th Annual American Association for the Advancement of Artificial Intelligence Conference (AAAI-11) [Student Poster], August 7-11, San Francisco, USA, 2011. (C)

16.   H. Wu, G. Qu, and X. Zhu, Self-Adjust Local Connectivity Analysis for Spectral Clustering, Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-11), Shenzhen, China, May 24-27, 2011. (C)

2010 (1-B, 5-J, and 5-C)

  1. Sorin Draghici, Taghi M. Khoshgoftaar, Vasile Palade, Witold Pedrycz, M. Arif Wani, Xingquan Zhu: The Ninth International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, USA, 12-14 December 2010 IEEE Computer Society 2010 (B)

2.      P. Zhang, X. Zhu, Y. Shi, L. Guo, and X. Wu, Robust Ensemble Learning for Mining Noisy Data Streams, Decision Support Systems, 50(2):469-479, 2011 (J)

3.      X. Zhu, P. Zhang, X. Lin, and Y. Shi, Active Learning from Stream Data Using Optimal Weight Classifier Ensemble, IEEE Trans. on Systems Man and Cybernetics, Part B, Part B, 40(6):1607-1621, December 2010 (J)

4.      Z. Zhu, Y. Guo, X. Zhu, and X. Xue, Normalized Dimensionality Reduction Using Nonnegative Matrix Factorization, Neurocomputing, 73(10-12):1783-1793, 2010, (J)

5.      A. Kamal, X. Zhu, A. Pandya, S. Hsu, and R. Narayanan, Feature Selection for Datasets with Imbalanced Class Distributions, International Journal of Software Engineering and Knowledge Engineering, accepted, 20(2):113-137, 2010.   (J)

6.      P. Zhang, X. Zhu, and Y. Shi, Multiple Criteria Programming Models for VIP E-Mail Behavior Analysis, Web Intelligence and Agent Systems, 8(1):69-78, 2010. (J)

7.      P. Zhang, X. Zhu, J. Tan, and L. Guo, Classifier and Cluster Ensembling for Mining Concept Drifting Data Streams, Proceedings of the 10th IEEE International Conference on Data Mining (ICDM-2010), Sydney, Australia, Dec. 14-17, 2010. (C)

8.      P. Zhang, X. Zhu, J. Tan, and L. Guo, SKIF: A Data Imputation Framework for Concept Drifting Data Streams, Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM-2010), Toronto, Canada, Oct. 26-30, 2010. (C)

9.      Z. Zhu, X. Zhu, Y. Guo, and X. Xue, Transfer Incremental Learning for Pattern Classification, Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM-2010), Toronto, Canada, Oct. 26-30, 2010. (C)

10.   Z. Lu, X. Wu, X. Zhu, and J. Bongard, Ensemble Pruning via Individual Contribution Ordering, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2010), Washington, DC, USA, July 25-28, 2010. (C)

11.   D. He, X. Wu, and X. Zhu, Rule Synthesizing from Multiple Irrelevant Databases Using Clustering, Proc. the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-10), Hyderabad, India, June 2010. (C)

2009 (1-P, 1-J, and 10-C)

1.      X. Zhu and X. Wu, Hybrid Frequent Pattern Tree for Discovering of Complex Relational-Patterns from Databases, United States Patent, Pending (P)

2.      X. Jiang and X. Zhu, vEye: Behavioral Footprinting for Self-Propagating Worm Detection and Profiling, Knowledge and Information Systems, 18(2):69-78, 2009 (J)

3.      X. Zhu, X. Wu, and C. Zhang, Vague One-Class Learning for Data Streams, Proc. of the 9th IEEE International Conference on Data Mining (ICDM-09), Miami, December 2009. (C)

4.      P. Zhang and X. Zhu, Mining Data Streams with Labeled and Unlabeled Training Examples, Proc. of the 9th IEEE International Conference on Data Mining (ICDM-09), Miami, December 2009. (C)

5.      D. He, X. Zhu, and X. Wu, Approximate Repeating Pattern Mining with Gap Requirements, Proc. of the 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI-09), Newark, New Jersey, Nov. 2009. (C)

6.      D. He, X. Zhu, and X. Wu, Error Detection and Uncertainty Modeling for Imprecise Data, Proc. of the 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI-09), Newark, New Jersey, Nov. 2009. (C)

7.      A. Kamal, X. Zhu, A. Pandya, and S. Hsu, Feature Selection with Biased Sample Distributions, Proc. of the IEEE International Conference on Information Reuse (IRI-09), Las Vegas, August 2009. (C)

8.      X. Zhu and R. Jin, Multiple Information Sources Cooperative Learning, Proc. of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), California, July, 2009 (C)

9.      P. Zhang, X. Zhu, Y, Shi, and X. Wu, An Aggregate Ensemble for Mining Data Streams with both Concept Drifting and Noise, Proc. Of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-09), Bangkok, April, 2009 (C)

10.   M. Slavik, X. Zhu, I. Mahgoub, and M. Shoaib, Parallel Selection of Informative Genes for Classification, Proc. Of the 1st International Conference on Bioinformatics and Computational Biology (BICoB-09), New Orleans, April, 2009 (C)

11.   A. Kamal, X. Zhu, A. Pandya, S. Hsu, and M. Shoaib, The Impact of Gene Selection on Imbalanced Microarray Expression Data, Proc. Of the 1st International Conference on Bioinformatics and Computational Biology (BICoB-09), New Orleans, April, 2009 (C)

12.   A. Kamal, X. Zhu, R. Narayanan, Gene Selection, for Microarray Expression Data with Imbalanced Sample Distributions, Proc.  of the International Joint Conference on Bioinformatics, Systems Biology and Intelligence Computing (IJCBS-09), Shanghai, China, August, 2009.

2008 (2-BC, 6-J and 10-C)

1.      X. Zhu,  Quantitative Association Rules, in Encyclopaedia of Database Systems, edited by Prof. Ling Liu and M. Tamer Oszu, Springer, 2008. (BC)

2.      X. Wu, Y. Zhang, and X. Zhu,  Data Mining , in Encyclopaedia of Computer Science and Engineering, edited by Benjamin W. Wah, Wiley, 2008. (BC)

3.      X. Zhu, R. Jin, Y. Breitbart, and G. Agrawal, MMIS-07, 08: Mining Multiple Information Sources Workshop Report, ACM SIGKDD Explorations, 10(2): 61-65, Dec., 2008. (J)

4.      X. Zhu, C. Zhang,  and D. Olson, Editorial: Special Issue on Data Mining, International Journal of Software and Informatics, 2(2): 89-94, Dec., 2008. (J)

5.      Y. Yang, X. Wu, and X. Zhu, Conceptual equivalence for contrast mining in classification learning, Data and Knowledge Engineering, 67(3): 413-429, Dec., 2008 (J)

6.      X. Zhu and Y. Yang, A Lazy Bagging Approach to Classification, Pattern Recognition, 41(10): 2980-2992, October, 2008. (J)

7.      X. Wu and X. Zhu, Mining with Noise Knowledge: Error Awareness Data Mining, IEEE Transactions on System, Man and Cybernetics, Part A, 38(4): 917-932, July, 2008. (J)

8.      G. Chen, X. Wu, and X. Zhu, Mining Sequential Patterns across Time Sequences, New Generation Computing, 26(1): 75-96, January, 2008. (J)

9.      X. Zhu, P. Zhang, X. Wu, D. He, C. Zhang, and Y. Shi, Cleansing Noisy Data Streams, Proc. Of the 8th IEEE International Conference on Data Mining (ICDM), Pisa, Italy, December, 2008. (C)

10.   X. Zhu, C. Bao, W. Qiu, Bagging Very Weak Learners with Lazy Local Learning, Proc. Of the 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, December, 2008. (C)

11.   P. Zhang, Y. Tian, Z. Zhang, A. Li, and X. Zhu, Select Objective Functions for Multiple Criteria Programming Classification, Proc. Of International Workshop on Optimization-based Data Mining and Web Intelligence (ODM, held in conjunction with the 2008 IEEE/WIC/ACM Joint International Conference on Web Intelligence (WI) and Intelligent Agent Technology (IAT)), Sydney, Australia, December, 2008. (C)

12.   X. Su, T. Khoshgoftaar, X. Zhu, VoB: Voting on Bagging Classifications, Proc. Of the 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, December, 2008. (C)

13.   P. Zhang, X. Zhu, Y. Shi, Categorizing and Mining Concept Drifting Data Stream, Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Las Vegas, August, 2008. (C)

14.   A. Kamal, X. Zhu, A. Pandya, S. Hsu, Y. Shi, An Empirical Study of Supervised Learning for Biological Sequence Profiling and Microarray Expression Data Analysis, Proc. Of the 2008 IEEE International Conference on Information Reuse and Integration (IRI), Las Vegas, July, 2008 (C)

15.   X. Su, T. M. Khoshgoftaar, X. Zhu, VCI Predictors: Voting on Classifications from Imputed Learning Sets, Proc. Of the 2008 IEEE International Conference on Information Reuse and Integration (IRI), Las Vegas, July, 2008 (C)

16.   C. Shah, X. Zhu, K. Khoshgoftaar, J. Beyer, Contrast Pattern Mining with Gap Constraints for Peptide Folding Prediction, Proc. of the 21st Florida Artificial Intelligence Research Society International Conference (FLAIRS), Florida, May, 2008. (C)

17.   Y. Lee, X. Zhu, A. Pandya, S. Hsu, iVESTA: An Interactive Visualization and Evaluation System for Drive Test Data, Proc. of the 23rd ACM Symposium on Applied Computing (SAC) , Brazil, March, 2008 (C)

18.   X. Su, T.M. Khoshgoftaar, X. Zhu, R. Greiner, Imputation-Boosted Collaborative Filtering Using Machine Learning Classifiers, Proc. of the 23rd Annual ACM Symposium on Applied Computing (SAC), Brazil, March, 2008 (C)

2007 (1-B, 2-J, and 11-C)

1.      X. Zhu and I. Davidson, Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data, Idea Group Inc. Publishing, 2007. (B)

2.      G. Mao, X. Wu, X. Zhu, G. Chen, and C. Liu, Mining maximal frequent itemsets from data streams, Journal of Information Science, 33: 251-262, June 2007. (J)

3.      X. Zhu, T. Khoshgoftaar, I. Davidson, and S. Zhang, Editorial: Special issue on mining low-quality data, Knowledge and Information Systems, 11(2): 131-136, February, 2007. (J)

4.      X. Zhu, Lazy learning for classifying imbalanced data, Proc. of the 7th IEEE International Conference on Data Mining (ICDM), Omaha,  October, 2007. (C)

5.      X. Zhu, P. Zhang, X. Lin, and Y. Shi, Active learning from data streams, Proc. of the 7th IEEE International Conference on Data Mining (ICDM), Omaha, October, 2007 (C)

6.      X. Su, R. Greiner, T. Khoshgoftaar, X. Zhu, Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts, Proc. of Web Intelligence (WI), 645-649, 2007. (C)

7.      X. Su, T. Khoshgoftaar, X. Zhu, A. Folleco, Rule-Based Multiple Object Tracking for Traffic Surveillance Using Collaborative Background Extraction, Proc. of International Symposium on Visual Computing (ISVC), (2), 469-478, 2007. (C)

8.      D. He, X. Wu, and X. Zhu, SAIL-APPROX: An Efficient On-line Algorithm for Approximate Pattern Matching with Wildcards and Length Constraints, Proc. of the 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), CA, November 2007. (C)

9.      Y. He, X. Wu, X. Zhu, and A. N. Arslan, Mining Frequent Patterns with Wildcards from Biological Sequences, Proc. of the 2007 IEEE International Conference on Information Reuse and Integration (IRI), Las Vegas, August 2007, USA. (C)

10.   X. Zhu and X. Wu: Discovering Relational Patterns across Multiple Databases, Proc. of the 23rd IEEE International Conference on Data Engineering (ICDE), pp.726-735, April 15-20, 2007. (C)

11.   J. Wang, Y. Liu, L. Zhou, Y. Shi, and X. Zhu, Pushing Frequency Constraint to Utility Mining Model. Proc. of the International Conference on Computational Science (ICCS), pp.685-692, May 27-30, 2007. (C)

12.   Y. Liu, P. Scheuermann, X. Li, and X. Zhu: Using WordNet to Disambiguate Word Senses for Text Classification. Proc. of the International Conference on Computational Science (ICCS), pp.781-789, May 27-30, 2007. (C)

13.   X. Zhu, X. Wu, T. Khoshgoftaar, and Y. Shi: An Empirical Study of the Noise Impact on Cost-Sensitive Learning, Proc. of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp.1168-1174, January 6-12, 2007. (C)

14.   X. Zhu ad X. Wu: Mining Complex Patterns across Sequences with Gap Requirements, Proc. of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp.1168-1174, January 6-12, 2007. (C)

2006 (5-J and 4-C)

  1. X. Zhu and X. Wu, Class Noise Handing for Effective Cost-Sensitive Learning by Cost-Guided Iterative Classification Filtering, IEEE Transactions on Knowledge and Data Engineering (TKDE) , vol.18, no.10, pp.1435-1440, 2006. (J)
  2. Y. Yang, X. Wu, and X. Zhu, Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams, Data Mining and Knowledge Discovery (DMKD) , vol.13, no.3, pp.261-289, 2006. (J)
  3. X. Zhu and X. Wu, Bridging Local and Global Data Cleansing: Identifying Class Noise in Large, Distributed Data Datasets, Data Mining and Knowledge Discovery (DMKD), vol.12, no.2, pp.275-308, 2006. (J)
  4. X. Zhu, X. Wu, and Y. Yang, Effective Classification of Noisy Data Streams with Attribute-Oriented Dynamic Classifier Selection, Knowledge and Information Systems (KAIS), vol.9, no.3, 2006. (J)
  5. G. Chen, X. Wu, X. Zhu, A. Arslan, and Y. He, Efficient string matching with wildcards and length constraints, Knowledge and Information Systems, 10(4): 399-419, November, 2006 (J)
  6. Y. Zhang, X. Zhu, and X. Wu, Corrective Classification: Classifier Ensembling with Corrective and Diverse Base Learners. Proc. of the 6th IEEE International Conference on Data Mining (ICDM), pp.1199-1204, 18-22 December, 2006, Hong Kong. (C)
  7. X. Zhu and X. Wu, Scalable Representative Instance Selection and Ranking. Proc. of the 18th International Conference on Pattern Recognition (ICPR), vol.3, pp.352-355, 20-24 August, 2006, Hong Kong. (C)
  8. X. Jiang, Y. Motai, R. Snapp, and X. Zhu, Accelerated Kernel Feature Analysis. Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp.109-116, 17-22 June 2006, New York, USA. (C)
  9. X. Zhu and X. Wu  Error Awareness Data Mining , Proc. of the IEEE International Conference on Granular Computing (GRC) , Atlanta, May 10-12, 2006, (C)

2005 (3-J and 5-C)

  1. X. Zhu and X. Wu, Cost-Constrained Data Acquisition for Intelligent Data Preparation, IEEE Trans. on Knowledge and Data Engineering (TKDE), vol.17, no.11, November, 2005. (J)
  2. X. Zhu, X. Wu, A. K. Elmagarmid, Z. Feng, and L. Wu, Video Data Mining: Semantic indexing and event detection from the association perspective, IEEE Trans. on Knowledge and Data Engineering (TKDE), vol.17, no.5, pp.665-677, May 2005. (J)
  3. X. Zhu, A. K. Elmagarmid, X. Xue, L. Wu, and A. Catlin, InsightVide: Towards hierarchical video content organization for efficient browsing, summarization and retrieval, IEEE Trans. on Multimedia, vol.7, no.4, 2005. (J)
  4. G. Chen, X. Wu, and X. Zhu, Sequential Pattern Mining in Multiple Data Streams, Proc. of the Fifth IEEE International Conference on Data Mining (ICDM '05), Houston, TX, USA, 27 - 30 November 2005. (C)
  5. Y. Zhang, X. Zhu, X. Wu, and J. P. Bond, ACE: An Aggressive Classifier Ensemble with Error Detection, Correction and Cleansing, Proc. of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Hong Kong, November 14-16, 2005. (C)
  6. Y. Yang, X. Wu, and X. Zhu, Mining in Anticipation: Proactive-Reactive Prediction for Data Streams, Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Chicago, IL, USA, August 21-24, 2005. (C)
  7. X. Jiang, Y. Motai, and X. Zhu, Predictive Fuzzy Control For A Mobile Robot With Nonholonomic Constraints, In Proceedings of the 12th International Conference on Advanced Robotics (ICAR 2005), Seattle, Washington, USA, July 18th-20th, 2005. (C)
  8. Q. Chen, X. Wu, and X. Zhu, Scalable Inductive Learning on Partitioned Data, In Proceedings of the 15th International Symposium on Methodologies for Intelligent Systems (ISMIS 2005), Saratoga Springs, NY, May 26 - 28, 2005. (C)

2004 (4-J, 1-BC, and 6-C)

1.      X. Zhu and X. Wu, Class noise vs attribute noise: a quantitative study of their impacts Artificial Intelligence Review, 22 (3-4):177-210, November 2004. (J)

2.      X. Zhu, X. Wu, J. Fan, A. Elmagarmid, and W. Aref, Exploring video content structure for hierarchical summarization, ACM/Springer Multimedia Systems, 10(2):98-115, 2004. (J)

3.      W. Aref, A. Catlin, A. Elmagarmid, J. Fan, M. Hammad, I. Ilyas, M. Marzouk, S. Prabhakar, and X. Zhu, A testbed facility for research in video database benchmarking , ACM Multimedia Systems Journal, Special Issue on Multimedia Document Management Systems, vol.9, no.6, 2004. (J)

4.      J. Fan, A. Elmagarmid, X. Zhu, W. Aref, and L. Wu, ClusterView: Hierarchical Video Shot Classification, indexing and accessing , IEEE Trans. on Multimedia, pp.70-86, vol.6, no.1, 2004 (J)

5.      J. Fan, X. Zhu, J. Xiao, Content-Based Video Retrieval, in Computer Graphics and Multimedia: Applications, Problems and Solutions, edited by Prof. John DiMarco, Idea Group Pub, Feb.,2004 (BC)

6.      X. Zhu and X. Wu, Data Acquisition with Active and Impact-Sensitive Instance Selection, Proc. of the 16th IEEE International Conf. on Tools with Artificial Intelligence (ICTAI 2004), FL, 2004. (C)

7.      X. Zhu, X. Wu and Y. Yang, Dynamic Classifier Selection for Effective Mining from Noisy Data Streams, Proc. of the 4th IEEE International Conference on Data Mining (ICDM 2004), UK, 2004. (C)

8.      X. Zhu and X. Wu, Cost-guided Class Noise Handling for Effective Cost-sensitive Learning , Proc. of the 4th IEEE International Conference on Data Mining (ICDM 2004), UK, 2004. (C)

9.      Y. Yang, X. Wu, X. Zhu, Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources, Proc. of 8th PKDD-2004, Pisa, Italy, 2004. (C)

10.   X. Zhu, X. Wu, Y. Yang, Error detection and impact-sensitive instance ranking in noisy datasets, Proc. of the 19th National Conference on Artificial Intelligence (AAAI-2004), July 25-29, California. (C)

11.   Q. Chen, X. Wu, X. Zhu, OIDM: Online Interactive Data Mining, Proc. of the 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-2004), May 17-20, 2004, Ottawa, Canada. (C)

2003 (3-J and 6-C)

1.      X. Zhu, J. Fan, A. K. Elmagarmid, and X. Wu, Hierarchical video summarization and content description joint semantic and visual similarity. ACM / Springer Multimedia System Journal, vol.9, no.1, pp.31-53, 2003. (J)

2.      E. Bertino, J. Fan, E. Ferrari, M.S. Hacid, A.K. Elmagarmid, and X. Zhu, A Hierarchical Access Control Model for Video Database Systems, ACM Trans. on Information Systems, 21(2), pp.155-191, 2003. (J)

3.      J. Fan, X. Zhu, K. Najarian, and L. Wu, Accessing video contents through key objects over IP, Journal of Multimedia Tools and Applications, vol.21, no.1, pp.75-96, 2003. (J)

4.      X. Zhu, X. Wu, Q. Chen, Eliminating class noise in large datasets, Proc. of the 20th ICML International Conference on Machine Learning (ICML-2003), Aug. 21-24, Washington D.C.,  2003. (C)

5.      X. Zhu, X. Wu, Mining video association for efficient database management, Proc. of 18th International Joint Conference on Artificial Intelligence (IJCAI-2003), Acapulco, Mexico. Aug.,12 -15, pp.1422-1424, 2003, (C)

6.      X. Zhu, X. Wu, Sequential association mining for video summarization, Proc. of IEEE International Conference on Multimedia & Expo (ICME-2003), Baltimore, MD. July, 6-9, 2003. (C)

7.      H. Luo, J. Fan, J. Xiao, X. Zhu, Semantic principal video shot classification via mixture Gaussian, Proc. of IEEE International Conf. on Multimedia & Expo (ICME 2003), MD. July, 6-9, 2003. (C)

8.      X. Zhu, W. G. Aref, J. Fan, A. C. Catlin, A. K. Elmagarmid, Medical video mining for efficient database indexing, management and access, Proc. of 19th IEEE International Conference on Data Engineering (ICDE-2003), India, March, 2003. (C)

9.      W. Aref,A. Catlin, A. K. Elmagarmid, J. Fan, M. Hammad, I. Ilyas, M. Marzouk, S. Prabhakar, Y. Tu, X. Zhu,  VDBMS: A testbed facility for research in video database benchmark , (invited paper), 9th International Conference on Distributed Multimedia Systems, Miami, Sept. 23-25, 2003. (C)

2002 (1-J and 13-C)

  1. J. Fan, X. Zhu, M.S. Hacid, and A. K. Elmagarmid, Content-based video classification toward hierarchical representation, indexing and accessing, Journal of Multimedia Tools and Applications, special issue on multimedia and internet, Vol. 17, No. 1, pp.97-120, 2002. (J)
  2. X. Zhu, J. Fan, H. Luo, M.S. Hacid, Using small samples for content-based image retrieval system with relevance feedback, Proc. of ACM Multimedia Workshop on Multimedia Information Retrieval (MIR-2002), Juan Les Pins, France, Dec., 2002. (C)
  3. J. Fan, H. Luo, X. Zhu, Semantic principal video shot classification system, Proc. of ACM Multimedia Workshop on Multimedia Information Retrieval (MIR-2002), Juan Les Pins, France, Dec., 2002. (C)
  4. X. Zhu, J. Fan, M.S. Hacid, A. K. Elmagarmid, ClassMiner: Mining medical video for scalable skimming and summarization, Proc. of 10th ACM International Conference on Multimedia, pp.79-80, France, Dec., 2002. (C)
  5. X. Zhu, J. Fan, X. Xue, L. Wu, A. K. Elmagarmid, Semi-automatic video annotation, Proc. of Third IEEE Pacific Rim Conference on Multimedia (PCM-2002), LNCS 2532, Springer, pp.245-252, Taiwan, Dec., 2002. (C)
  6. X. Zhu, X. Xue, J. Fan, L. Wu, Qualitative camera motion classification for content-based video indexing, Proc. of Third IEEE Pacific Rim Conference on Multimedia (PCM-2002), LNCS 2532, pp.1128-1136, Taiwan, Dec., 2002. (C)
  7. X. Zhu, J. Fan, W. G. Aref, A. K. Elmagarmid, ClassMiner: Mining medical video content structure and events towards efficient access and scalable skimming, Proc. of ACM SIGMOD Workshop on Data Mining and Knowledge Discovery (DMKD-2002), pp.9-19, June, Madison, WI, 2002. (C)
  8. X. Xue, X. Zhu, Y. Xiao, L. Wu, Using mutual relationship between motion vectors for qualitative camera motion classification in MPEG video. Proc. of SPIE: Second International Conference on Image and Graphics (ICIG-2002), Vol.4875, pp.853-860, Anhui, Aug., 2002. (C)
  9. X. Zhu, J. Fan, A. K. Elmagarmid, Towards facial feature locating and verification for omni-face detection in video/images, Proc. of IEEE International Conference on Image Processing (ICIP-2002), vol.2, pp.113-116, NY, Sept., 2002. (C)
  10. X. Zhu, J. Fan, A. K. Elmagarmid, W. G. Aref Hierarchical video summary for medical data, Proc. SPIE: Storage and Retrieval for Media Databases 2002 (SPIE-2002), vol. 4676, pp.395-406, San Jose, Jan. 2002. (C)
  11. J. Fan, M. Body, X. Zhu, M.S. Hacid, Seeded image segmentation for content-based image retrieval application, Proc. SPIE: Storage and Retrieval for Media Databases 2002 (SPIE-2002), vol.4676, pp.10-21, San Jose, Jan. 2002. (C)
  12. X. Lu, Z. Feng, X. Zhu, L. Wu, News story segmentation based on a simple statistic model, Proc SPIE: Internet Imaging III 2002 (SPIE-2002), vol.4672, pp.261-268, San Jose, Jan 2002. (C)
  13. W.G. Aref, A. Catlin, A.K. Elmagarmid, J. Fan, M. Hammad, I. Ilyas, M. Marzouk, X. Zhu A video database management system for advancing video database research, International Workshop on Multimedia Information Systems (MIS-2002), Tempe, Arizona, USA, Oct.30-Nov.1, 2002. (C)
  14. W. G. Aref,  A.C. Catlin, A. K. Elmagarmid, J. Fan, J. Guo, M. Hammad, I.F. Ilyas, M.S. Marzouk, S. Prabhakar, A. Rezgui, S. Teoh, E. Terzi, Y. Tu, A. Vakali, X. Zhu, A Distributed Database Server for Continuous Media, 18th IEEE International Conference on Data Engineering (ICDE-202), pp. 490-491, San Jose, California. (C)

2001 (3-J, 1-P, and 3-C)

  1. X. Zhu, H. Zhang, Liu W., C. Hu, and L. Wu, A new query refinement and semantics integrated image retrieval system with semi-automatic annotation scheme, Journal of Electronic Imaging, Special Issue on Storage, Processing and Retrieval of Digital Media, Vol.10 (4), pp.850-850, 2001. (J)
  2. J. Fan, X. Zhu, and L. Wu, Automatic model-based semantic object extraction algorithm, IEEE Trans. on Circuits and Systems for Video Technology, vol.11, no.10, pp.1073-1084, Oct., 2001. (J)
  3. J. Fan, W. G. Aref, A. K. Elmagarmid, M.S. Hacid, M.S. Marzouk, and X. Zhu, MultiView: multi-level video content representation and retrieval, Journal of Electronic Imaging, Special Issue on Storage, Processing and Retrieval of Digital Media, Vol. 10 (4).  2001. (J)

4.      Relevance Maximizing, Iteration Minimizing, Relevance-Feedback, Content-Based Image Retrieval (CBIR). H. Zhang, Z. Su, X. Zhu, United States Patent, 6748398. (P)

  1. X. Zhu, L. wu, X. Xue, X. Lu, J. Fan. Automatic Scene Detection in News Program by Integrating Visual Feature and Rules, Proc. of the second IEEE Pacific-Rim conference on multimedia (PCM-2001), LNCS2195 , Springer, pp.837-842, Beijing, Oct. 24-26, 2001. (C)
  2. J. Fan, X. Zhu, L. Wu. Seeded Semantic Object Generation Toward Content-Based Video Indexing, Proc. of the second IEEE Pacific-Rim conference on multimedia (PCM-2001), LNCS2195, Springer, pp. 843-848, Beijing, Oct. 24-26, 2001. (C)
  3. X. Zhu, Liu W., H. Zhang, L. Wu, An image retrieval and semi-automatic annotation scheme for large image databases on the Web, Proc. 13th SPIE symposium on Electronic Imaging-EI24 Internet Imaging II (SPIE-2001), Vol.4311, pp.168-177 Jan. 2001, San Jose. (C)

2000 (1-C)

  1. Y. Lu, C. Hu, X. Zhu, H. Zhang, Q. Yang, A Unified Semantics and Feature Based Image Retrieval Technique Using Relevance Feedback, Proc. of the 8th ACM International Conference on Multimedia (ACM MM-2000), pp. 31 - 37 LA, California, October, 2000. (C)