Zhi-Hua Zhou's Publications

Zhi-Hua Zhou's Publications

[Selected International Publications][Full List][List by Topic] [Selected Native Publications]

ML/DM Topics: [Multi-Label Learning] [Multi-Instance Learning] [Semi-Supervised and Active Learning] [Cost-Sensitive and Class-Imbalance Learning] [Metric Learning, Dimensionality Reduction and Feature Selection] [Ensemble Learning] [Structure Learning and Clustering]

Applications: [Image Retrieval] [Web Search and Mining] [Face Recognition] [Computer-Aided Medical Diagnosis] [Bioinformatics]

Other Topics: [Theoretical Aspects of Evolutionary Computation] [Improving Comprehensibility]

[Miscellaneous]

Note: The same article may appear under different topics

Multi-Label Learning

Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data, in press. [code & data]
Y.-X. Li, S. Ji, J. Ye, S. Kumar, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1445-1450. [code]
R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.896-902.
M.-L. Zhang and Z.-H. Zhou. M ³ MIML: A maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.688-697. [code][data]
Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, Y.-F. Li. MIML: A framework for learning with ambiguous objects. CORR abs/0808.3231, 2008.
Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependency maximization. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, pp1503-1505. (short paper) [code & data]
M.-L. Zhang and Z.-H. Zhou. Multi-label learning by instance differentiation. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI'07), Vancouver, Canada, 2007, pp.669-674. [code]
Z.-H. Zhou. Mining ambiguous data with multi-instance multi-label learning. In: Proceedings of the 3rd International Conference on Advanced Data Mining and Applications (ADMA'07), Harbin, China, LNAI 4632, 2007, pp.1. Invited plenary talk at ADMA'07
M.-L. Zhang and Z.-H. Zhou. ML-kNN: A lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40(7): 2038-2048. [code]
Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In: Advances in Neural Information Processing Systems 19 (NIPS'06) (Vancouver, Canada), B. Schölkopf, J. C. Platt, and T. Hofmann, eds. Cambridge, MA: MIT Press, 2007, pp.1609-1616. [code][data]
M.-L. Zhang and Z.-H. Zhou. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 1338-1351. [code]
M.-L. Zhang and Z.-H. Zhou. A k -nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing (GrC'05), Beijing, China, 2005, pp.718-721.

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Multi-Instance Learning

Y.-F. Li, J. T. Kwok, I. W. Tsang, and Z.-H. Zhou. A convex method for locating regions of interest with multi-instance learning. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'09), Bled, Slovenia, Part II, LNAI 5782, 2009, pp.15-30. [code]
Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. Multi-instance learning by treating instances as non-i.i.d. samples. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1249-1256. (CORR abs/0807.1997) [code][data]
Y.-X. Li, S. Ji, J. Ye, S. Kumar, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1445-1450. [code]
R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.896-902.
M.-L. Zhang and Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68. [code]
M.-L. Zhang and Z.-H. Zhou. M ³ MIML: A maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.688-697. [code][data]
Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, Y.-F. Li. MIML: A framework for learning with ambiguous objects. CORR abs/0808.3231, 2008.
Z.-H. Zhou and J.-M. Xu. On the relation between multi-instance learning and semi-supervised learning. In: Proceedings of the 24th International Conference on Machine Learning (ICML'07), Corvallis, OR, 2007, pp.1167-1174. [slides][code]
Z.-H. Zhou. Mining ambiguous data with multi-instance multi-label learning. In: Proceedings of the 3rd International Conference on Advanced Data Mining and Applications (ADMA'07), Harbin, China, LNAI 4632, 2007, pp.1. Invited plenary talk at ADMA'07
Z.-H. Zhou and M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]
Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In: Advances in Neural Information Processing Systems 19 (NIPS'06) (Vancouver, Canada), B. Schölkopf, J. C. Platt, and T. Hofmann, eds. Cambridge, MA: MIT Press, 2007, pp.1609-1616. [code][data]
M.-L. Zhang and Z.-H. Zhou. Adapting RBF neural networks to multi-instance learning. Neural Processing Letters, 2006, 23(1): 1-26. [code]
Z.-H. Zhou. Multi-instance learning from supervised view. Journal of Computer Science and Technology, 2006, 21(5): 800-809. Invited paper
Z.-H. Zhou, X.-B. Xue, and Y. Jiang. Locating regions of interest in CBIR with multi-instance learning techniques. In: Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AJCAI'05), Sydney, Australia, LNAI 3809, 2005, pp.92-101.
Z.-H. Zhou, K. Jiang, and M. Li. Multi-instance learning based web mining. Applied Intelligence, 2005, 22(2): 135-147. [data]
Z.-H. Zhou. Multi-instance learning: A survey. Technical Report, AI Lab, Department of Computer Science & Technology, Nanjing University, Nanjing, China, Mar. 2004.
M.-L. Zhang and Z.-H. Zhou. Improve multi-instance neural networks through feature selection. Neural Processing Letters, 2004, 19(1): 1-10. [code]
Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Proceedings of the 14th European Conference on Machine Learning (ECML'03), Cavtat-Dubrovnik, Croatia, LNAI 2837, 2003, pp.492-502. [code]
Z.-H. Zhou, M.-L. Zhang, and K.-J. Chen. A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), Sacramento, CA, 2003, pp.565-569.
Z.-H. Zhou and M.-L. Zhang. Neural networks for multi-instance learning. Technical Report, AI Lab, Department of Computer Science & Technology, Nanjing University, Nanjing, China, Aug. 2002. [code]

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Semi-Supervised and Active Learning

J. Du, C. X. Ling, and Z.-H. Zhou. When does co-training work in real data? IEEE Transactions on Knowledge and Data Engineering, in press.
Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowledge and Information Systems, in press.
J.-M. Xu, G. Fumera, F. Roli, and Z.-H. Zhou. Training SpamAssassin with active semi-supervised learning. In: Proceedings of the 6th Conference on Email and Anti-Spam (CEAS'09), Mountain View, CA, 2009.
Y.-F. Li, J. T. Kwok, and Z.-H. Zhou. Semi-supervised learning using label mean. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.633-640. [code]
D.-C. Zhan, M. Li, Y.-F. Li, and Z.-H. Zhou. Learning instance specific distances using metric propagation. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1225-1232. [code]
Y. Zhang and Z.-H. Zhou. Non-metric label propagation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1357-1362. [code]
Z.-H. Zhou. When semi-supervised learning meets ensemble learning. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems (MCS'09), Reykjavik, Iceland, LNCS 5519, 2009, pp.529-538. [slides] Invited plenary talk at MCS'09
Z.-H. Zhou, M. Ng, Q.-Q. She, and Y. Jiang. Budget semi-supervised learning. In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Bangkok, Thailand, LNAI 5476, 2009, pp.588-595.
C. X. Ling, J. Du, and Z.-H. Zhou. When does co-training work in real data? In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Bangkok, Thailand, LNAI 5476, 2009, pp.596-603.
M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 2009, 45(3): 341-355.
Z.-H. Zhou. Semi-supervised learning by disagreement. In: Proceedings of the 4th IEEE International Conference on Granular Computing (GrC'08), Hangzhou, China, 2008, pp.93. Invited plenary talk at GrC'08
W. Wang and Z.-H. Zhou. On multi-view active learning and the combination with semi-supervised learning. In: Proceedings of the 25th International Conference on Machine Learning (ICML'08), Helsinki, Finland, 2008, pp.1152-1159.
Z.-H. Zhou, D.-C. Zhan, and Q. Yang. Semi-supervised learning with very few labeled training examples. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI'07), Vancouver, Canada, 2007, pp.675-680. [code]
Z.-H. Zhou and J.-M. Xu. On the relation between multi-instance learning and semi-supervised learning. In: Proceedings of the 24th International Conference on Machine Learning (ICML'07), Corvallis, OR, 2007, pp.1167-1174. [slides][code]
W. Wang and Z.-H. Zhou. Analyzing co-training style algorithms. In: Proceedings of the 18th European Conference on Machine Learning (ECML'07), Warsaw, Poland, LNAI 4701, 2007, pp.454-465.
D. Zhang, Z.-H. Zhou, and S. Chen. Semi-supervised dimensionality reduction. In: Proceedings of the 7th SIAM International Conference on Data Mining (SDM'07), Minneapolis, MN, 2007, pp.629-634.
Z.-H. Zhou and M. Li. Semisupervised regression with co-training style algorithms. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(11): 1479-1493. [code]
M. Li and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 2007, 37(6): 1088-1098. [code]
Z.-H. Zhou. Learning with unlabeled data and its application to image retrieval. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.5-10. Keynote speech at PRICAI'06
Z.-H. Zhou, K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 2006, 24(2): 219-244.
Z.-H. Zhou and M. Li. Semi-supervised regression with co-training. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), Edinburgh, Scotland, 2005, pp.908-913.
M. Li and Z.-H. Zhou. SETRED: Self-training with editing. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, LNAI 3518, 2005, pp.611-621.
Z.-H. Zhou and M. Li. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529-1541. [code]
Z.-H. Zhou, K.-J. Chen, and Y. Jiang. Exploiting unlabeled data in content-based image retrieval. In: Proceedings of the 15th European Conference on Machine Learning (ECML'04), Pisa, Italy, LNAI 3201, 2004, pp.525-536.

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Cost-Sensitive and Class-Imbalance Learning

Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press. [code]
Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning. Computational Intelligence, in press. [code]
X.-Y. Liu and Z.-H. Zhou. Learning with cost intervals. In: Working Notes of the Workshop on Data Mining When Imbalanced Classes and Errors in Costs (ICEC'09), in conjunction with PAKDD'09, Bangkok, Thailand, 2009, pp.26-37.
X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550. [code]
L.-P. Liu, Y. Yu, Y. Jiang, and Z.-H. Zhou. TEFE: A time-efficient approach to feature extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.423-432.
Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, AK, 2008. [code]
Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.567-572. [code]
X.-Y. Liu and Z.-H. Zhou. The influence of class imbalance on cost-sensitive learning: An empirical study. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.970-974.
X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory under-sampling for class-imbalance learning. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.965-969. [code]
Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 63-77. [code]

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Metric Learning, Dimensionality Reduction and Feature Selection

Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data, in press. [code & data]
J. Liu, S. Chen, Z.-H. Zhou, and X. Tan. Generalized low rank approximations of matrices revisited. IEEE Transactions on Neural Networks, in press.
L.-P. Liu, Y. Jiang, and Z.-H. Zhou. Least square incremental linear discriminant analysis. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09), Miami, FL, 2009, pp.298-306.
X. Geng, K. Smith-Miles, Z.-H. Zhou, and L. Wang. Face image modeling by multilinear subspace analysis with missing values. In: Proceedings of the 17th ACM International Conference on Multimedia (ACM Multimedia'09), Beijing, China, 2009, pp.629-632. (short paper)
D.-C. Zhan, M. Li, Y.-F. Li, and Z.-H. Zhou. Learning instance specific distances using metric propagation. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1225-1232. [code]
Y. Zhang and Z.-H. Zhou. Non-metric label propagation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1357-1362.
M. Li, X.-B. Xue, and Z.-H. Zhou. Exploiting multi-modal interactions: A unified framework. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1120-1125.
R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.896-902.
L.-P. Liu, Y. Yu, Y. Jiang, and Z.-H. Zhou. TEFE: A time-efficient approach to feature extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.423-432.
Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependency maximization. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, pp.1503-1505. (short paper) [code & data]
D. Zhang, S. Chen, and Z.-H. Zhou. Constraint score: A new filter method for feature selection with pairwise constraints. Pattern Recognition, 2008, 41(5): 1440-1451.
D. Zhang, Z.-H. Zhou, and S. Chen. Semi-supervised dimensionality reduction. In: Proceedings of the 7th SIAM International Conference on Data Mining (SDM'07), Minneapolis, MN, 2007, pp.629-634.
D. Zhang, Z.-H. Zhou, and S. Chen. Adaptive kernel principal component analysis with unsupervised learning of kernels. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.1178-1182.
D.-C. Zhan and Z.-H. Zhou. Neighbor line-based locally linear embedding. In: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'06), Singapore, LNAI 3918, 2006, pp.606-615.
D. Zhang, Z.-H. Zhou, and S. Chen. Non-negative matrix factorization on kernels. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.404-412. [data] This paper won the Best Paper Award at PRICAI'06
J. Zhang, L. He, and Z.-H. Zhou. Ensemble-based discriminant manifold learning for face recognition. In: Proceedings of the 2nd International Conference on Natural Computation (ICNC'06), Chongqing, China, LNCS 4221, 2006, pp.29-38.
D. Zhang, Z.-H. Zhou, and S. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, 2006, 39(1): 140-142. [data]
X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Feature selection for high dimensional face image using self-organizing maps. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, LNAI 3518, 2005, pp.500-504.
D. Zhang, S. Chen, and Z.-H. Zhou. Two-dimensional non-negative matrix factorization for face representation and recognition. In: Proceedings of the 2nd International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'05), in Conjunction with ICCV'05, Beijing, China, LNCS 3723, 2005, pp.350-363. [data]
X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, 35(6): 1098-1107. [code]
D. Zhang and Z.-H. Zhou. (2D)² PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neurocomputing, 2005, 69(1-3): 224-231. [data]
J. Zhang, H. Shen, and Z.-H. Zhou. Unified locally linear embedding and linear discriminant analysis algorithm (ULLELDA) for face recognition. In: Proceedings of the 5th Chinese Conference on Biometric Recognition (Sinobiometrics'04), Guangzhou, China, LNCS 3338, 2004, pp.296-304.

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Ensemble Learning

Z.-H. Zhou and N. Li. Multi-information ensemble diversity. In: Proceedings of the 9th International Workshop on Multiple Classifier Systems (MCS'10), LNCS 5997, Cairo, Egypt, 2010, pp.134-144.
M.-L. Zhang and Z.-H. Zhou. Classifier ensemble with unlabeled data. CORR abs/0909.3593, 2009.
Y. Yu and Z.-H. Zhou. A framework for modeling positive class expansion with single snapshot. Knowledge and Information Systems, in press. [slides][code&data]
M. Li, W. Wang, and Z.-H. Zhou. Exploiting remote learners in internet environment with agents. Science China: Information Sciences, 2010, 53(1): 64-76.
N. Li, Y. Yu, and Z.-H. Zhou. Semi-naive exploitation of one-dependence estimators. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09), Miami, FL, 2009, pp.278-287.
Z.-H. Zhou. When semi-supervised learning meets ensemble learning. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems (MCS'09), Reykjavik, Iceland, LNCS 5519, 2009, pp.529-538. [slides] Invited plenary talk at MCS'09
N. Li and Z.-H. Zhou. Selective ensemble under regularization framework. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems (MCS'09), Reykjavik, Iceland, LNCS 5519, 2009, pp.293-303. [code]
X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550. [code]
Z.-H. Zhou. Ensemble learning. In: S. Z. Li ed. Encyclopedia of Biometrics, Berlin: Springer, 2009, 270-273.
Z.-H. Zhou. Ensemble. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 988-991.
Z.-H. Zhou. Boosting. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 260-263.
Z.-H. Zhou and Yang Yu. AdaBoost. In: X. Wu and V. Kumar eds. The Top Ten Algorithms in Data Mining, Boca Raton, FL: Chapman & Hall, 2009, 127-149.
Y. Jiang, M. Li, and Z.-H. Zhou. Mining extremely small data sets with application to software reuse. Software: Practice and Experience, 2009, 39(4): 423-440.
F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.413-422. [code] This paper won the Theoretical/Algorithms Runner-Up Best Paper Award at IEEE ICDM'08
D. Zhang, S. Chen, Z.-H. Zhou, and Q. Yang. Constraint projections for ensemble learning. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, pp.758-763.
L. Wang, M. Sugiyama, C. Yang, Z.-H. Zhou, and J. Feng. On the margin explanation of boosting algorithm. In: Proceedings of the 21st Annual Conference on Learning Theory (COLT'08), Helsinki, Finland, 2008, pp.479-490.
Y. Yu and Z.-H. Zhou. A framework for modeling positive class expansion with single snapshot. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, 2008, pp.429-440. [slides][code&data] This paper won the Best Paper Award at PAKDD'08
F. T. Liu, K. M. Ting, Y. Yu, and Z.-H. Zhou. Spectrum of variable-random trees. Journal of Artificial Intelligence Research, 2008, 32: 355-384.
Y. Yu, Z.-H. Zhou, and K. M. Ting. Cocktail ensemble for regression. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07), Omeha, NE, 2007, pp.721-726.
M. Li and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 2007, 37(6): 1088-1098. [code]
Z.-H. Zhou and M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]
Y. Yu, D.-C. Zhan, X.-Y. Liu, M. Li, and Z.-H. Zhou. Predicting future customers via ensembling gradually expanded trees. International Journal of Data Warehousing and Mining, 2007, 3(2): 12-21. Invited paper for the PAKDD'06 Data Mining Competition (Open Category) Grand Champion Team
X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory under-sampling for class-imbalance learning. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.965-969. [code]
Y. Jiang, M. Li, and Z.-H. Zhou. Generation of comprehensible hypotheses from gene expression data. In: Proceedings of the International Workshop on Data Mining for Biomedical Application (BioDM'06), in conjunction with PAKDD'06, Singapore, LNBI 3916, 2006, pp.116-123.
J. Zhang, L. He, and Z.-H. Zhou. Ensemble-based discriminant manifold learning for face recognition. In: Proceedings of the 2nd International Conference on Natural Computation (ICNC'06), Chongqing, China, LNCS 4221, 2006, pp.29-38.
Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 63-77. [code]
Z.-H. Zhou and W. Tang. Clusterer ensemble. Knowledge-Based Systems, 2006, 19(1): 77-83. [code]
X. Geng and Z.-H. Zhou. Image region selection and ensemble for face recognition. Journal of Computer Science and Technology, 2006, 21(1): 116-125. [data]
Z.-H. Zhou. Multi-instance learning from supervised view. Journal of Computer Science and Technology, 2006, 21(5): 800-809. Invited paper
Y. Jiang, J.-J. Ling, G. Li, H. Dai, and Z.-H. Zhou. Dependency bagging. In: Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'05), Regina, Canada, LNAI 3641, 2005, pp.491-500.
N. Li, H.-J. Zhou, J.-J. Ling, and Z.-H. Zhou. Spiculated lesion detection in digital mammogram based on artificial neural network ensemble. In: Proceedings of the 2nd International Symposium on Neural Networks (ISNN'05), Chongqing, China, LNCS 3498, 2005, pp.790-795.
X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble. IEEE Transactions on Neural Networks, 2005, 16(4): 875-886. [code][data]
Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, 35(4): 725-735. [code]
Z.-H. Zhou and Y. Yu. Adapt bagging to nearest neighbor classifiers. Journal of Computer Science and Technology, 2005, 20(1): 48-54. [detailed result] Invited paper
Y. Jiang and Z.-H. Zhou. Editing training data for kNN classifiers with neural network ensemble. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.356-361.
Z.-H. Zhou and Y. Jiang. NeC4.5: Neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(6): 770-773. [code]
Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Proceedings of the 14th European Conference on Machine Learning (ECML'03), Cavtat-Dubrovnik, Croatia, LNAI 2837, 2003, pp.492-502. [code]
Z.-H. Zhou and W. Tang. Selective ensemble of decision trees. In: Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'03), Chongqing, China, LNAI 2639, 2003, pp.476-483.
Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42. [code]
Z.-H. Zhou, Y. Jiang, and S.-F. Chen. Extracting symbolic rules from trained neural network ensembles. AI Communications, 2003, 16(1): 3-15.
Z.-H. Zhou, J. Wu, and W. Tang. Ensembling neural networks: Many could be better than all. Artificial Intelligence, 2002, 137(1-2): 239-263. [code]
Z.-H. Zhou, Y. Jiang, Y.-B. Yang, and S.-F. Chen. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine, 2002, 24(1): 25-36.
Z.-H. Zhou, J.-X. Wu, Y. Jiang, and S.-F. Chen. Genetic algorithm based selective neural network ensemble. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, WA, 2001, vol.2, pp.797-802. This paper was nominated along with other four papers selected from 796 submissions for the Distinguished Paper Award at IJCAI'01

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Structure Learning and Clustering

Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. Multi-instance learning by treating instances as non-i.i.d. samples. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1249-1256. (CORR abs/0807.1997) [code][data]
Y.-F. Li, I. W. Tsang, J. Kwok, and Z.-H. Zhou. Tighter and convex maximum margin clustering. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS'09), Clearwater Beach, FL, 2009, pp.328-335. [code]
M.-L. Zhang and Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68. [code]
X.-L. Li and Z.-H. Zhou. Structure learning of probabilistic relational models from incomplete relational data. In: Proceedings of the 18th European Conference on Machine Learning (ECML'07), Warsaw, Poland, LNAI 4701, 2007, pp.214-225.
D. Zhang, S. Chen, and Z.-H. Zhou. Entropy-inspired competitive clustering algorithms. International Journal of Software Informatics, 2007, 1(1): 67-84.
Z.-H. Zhou and M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170. [code]
Z.-H. Zhou and W. Tang. Clusterer ensemble. Knowledge-Based Systems, 2006, 19(1): 77-83. [code]

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Image Retrieval

Y.-F. Li, J. T. Kwok, I. W. Tsang, and Z.-H. Zhou. A convex method for locating regions of interest with multi-instance learning. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'09), Bled, Slovenia, Part II, LNAI 5782, 2009, pp.15-30. [code]
D.-C. Zhan, M. Li, Y.-F. Li, and Z.-H. Zhou. Learning instance specific distances using metric propagation. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1225-1232. [code]
M. Li, X.-B. Xue, and Z.-H. Zhou. Exploiting multi-modal interactions: A unified framework. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1120-1125.
Y. Guo, F. Liu, J. Shi, Z.-H. Zhou, and M. Gleicher. Image retargeting using mesh parametrization. IEEE Transactions on Multimedia, 2009, 11(5): 856-867.
R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.896-902.
L.-P. Liu, Y. Yu, Y. Jiang, and Z.-H. Zhou. TEFE: A time-efficient approach to feature extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.423-432.
Z.-H. Zhou, D.-C. Zhan, and Q. Yang. Semi-supervised learning with very few labeled training examples. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI'07), Vancouver, Canada, 2007, pp.675-680. [code]
Z.-H. Zhou. Mining ambiguous data with multi-instance multi-label learning. In: Proceedings of the 3rd International Conference on Advanced Data Mining and Applications (ADMA'07), Harbin, China, LNAI 4632, 2007, pp.1. [slides] Invited plenary talk at ADMA'07
Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In: Advances in Neural Information Processing Systems 19 (NIPS'06) (Vancouver, Canada), B. Schölkopf, J. C. Platt, and T. Hofmann, eds. Cambridge, MA: MIT Press, 2007, pp.1609-1616. [code] [data]
Z.-H. Zhou and H.-B. Dai. Query-sensitive similarity measure for content-based image retrieval. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06), Hong Kong, China, 2006, pp.1211-1215.
Z.-H. Zhou. Learning with unlabeled data and its application to image retrieval. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.5-10. Keynote speech at PRICAI'06
Z.-H. Zhou, K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 2006, 24(2): 219-244.
A. Ghafoor, Z. Zhang, M. S. Lew, and Z.-H. Zhou. Machine learning approaches to multimedia information retrieval. ACM/Springer Multimedia Systems, 2006, 12(1): 1-2. this article was the editorial to the special issue Machine Learning Approaches to Multimedia Information Retrieval edited by A. Ghafoor, Z. Zhang, M. S. Lew, and Z.-H. Zhou
Z.-H. Zhou, X.-B. Xue, and Y. Jiang. Locating regions of interest in CBIR with multi-instance learning techniques. In: Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AJCAI'05), Sydney, Australia, LNAI 3809, 2005, pp.92-101.
Z.-H. Zhou, K.-J. Chen, and Y. Jiang. Exploiting unlabeled data in content-based image retrieval. In: Proceedings of the 15th European Conference on Machine Learning (ECML'04), Pisa, Italy, LNAI 3201, 2004, pp.525-536.
Z.-H. Zhou, M.-L. Zhang, and K.-J. Chen. A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), Sacramento, CA, 2003, pp.565-569.

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Web Search and Mining

M. Li, W. Wang, and Z.-H. Zhou. Exploiting remote learners in internet environment with agents. Science China: Information Sciences, 2010, 53(1): 64-76.
J.-M. Xu, G. Fumera, F. Roli, and Z.-H. Zhou. Training SpamAssassin with active semi-supervised learning. In: Proceedings of the 6th Conference on Email and Anti-Spam (CEAS'09), Mountain View, CA, 2009.
M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 2009, 45(3): 341-355.
X.-B. Xue and Z.-H. Zhou. Distributional features for text categorization. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(3): 428-442.
L.-P. Liu, Y. Yu, Y. Jiang, and Z.-H. Zhou. TEFE: A time-efficient approach to feature extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.423-432.
M. Li, Z. Zhang, and Z.-H. Zhou. Mining bulletin board systems using community generation. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, 2008, pp.209-221.
X.-B. Xue, Z.-H. Zhou, and Z. Zhang. Improving web search using image snippets. ACM Transactions on Internet Technology, 2008, 8(4): Article 21. [illustration]
Z.-H. Zhou and H.-B. Dai. Exploiting image contents in web search. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI'07), Hyderabad, India, 2007, pp.2928-2933.
X.-B. Xue, Z.-H. Zhou, and Z. Zhang. Improve web search using image snippets. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.1431-1436. [illustration]
X.-B. Xue and Z.-H. Zhou. Distributional features for text categorization. In: Proceedings of the 17th European Conference on Machine Learning (ECML'06), Berlin, Germany, LNAI 4212, 2006, pp.497-508.
Z.-H. Zhou, K. Jiang, and M. Li. Multi-instance learning based web mining. Applied Intelligence, 2005, 22(2): 135-147. [data]

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Face Recognition

Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press. [code]
X. Geng, K. Smith-Miles, Z.-H. Zhou, and L. Wang. Face image modeling by multilinear subspace analysis with missing values. In: Proceedings of the 17th ACM International Conference on Multimedia (ACM Multimedia'09), Beijing, China, 2009, pp.629-632. (short paper)
X. Tan, F. Song, Z.-H. Zhou, and S. Chen. Enhanced pictorial structures for precise eye localization under uncontrolled conditions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.1621-1628.
X. Tan, S. Chen, Z.-H. Zhou, and J. Liu. Face recognition under occlusions and variant expressions with partial similarity. IEEE Transactions on Information Forensics & Security, 2009, 4(2): 217-230.
X. Geng, K. Smith-Miles, and Z.-H. Zhou. Facial age estimation by nonlinear aging pattern subspace. In: Proceedings of the 16th ACM International Conference on Multimedia (ACM Multimedia'08), Vancouver, Canada, 2008, pp.721-724. (short paper)
Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, AK, 2008. [code]
X. Geng, Z.-H. Zhou, and K. Smith-Miles. Individual stable space: An approach to face recognition under uncontrolled conditions. IEEE Transactions on Neural Networks, 2008, 19(8): 1354-1368.
Liu, S. Chen, Z.-H. Zhou, and X. Tan. Single image subspace for face recognition. In: Proceedings of the 3rd International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'07), in Conjunction with ICCV'07, Rio de Janeiro, Brazil, LNCS 4778, 2007, pp.205-219.
X. Geng, Z.-H. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(12): 2234-2240. This paper was listed as the "Featured article" of the vol.29 no.12 issue of TPAMI
X. Tan, S. Chen, Z.-H. Zhou, and J. Liu. Learning non-metric partial similarity based on maximal margin criterion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), New York, NY, 2006, pp.138-145. [code]
X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, and H. Dai. Learning from facial aging patterns for automatic age estimation. In: Proceedings of the 14th ACM International Conference on Multimedia (ACM Multimedia'06), Santa Barbara, CA, 2006, pp.307-316.
D. Zhang, Z.-H. Zhou, and S. Chen. Non-negative matrix factorization on kernels. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.404-412. [data] This paper won the Best Paper Award at PRICAI'06
X. Geng, Z.-H. Zhou, and H. Dai. Uncontrolled face recognition by individual stable neural network. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.553-562.
D. Zhang, S. Chen, and Z.-H. Zhou. Recognizing face or object from a single image: Linear vs. kernel methods on 2D patterns. In: Proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR'06), in conjunction with ICPR'06, Hong Kong, China, LNCS 4109, 2006, pp.889-897. [data]
J. Zhang, L. He, and Z.-H. Zhou. Ensemble-based discriminant manifold learning for face recognition. In: Proceedings of the 2nd International Conference on Natural Computation (ICNC'06), Chongqing, China, LNCS 4221, 2006, pp.29-38.
D. Zhang, Z.-H. Zhou, and S. Chen. Diagonal principal component analysis for face recognition. Pattern Recognition, 2006, 39(1): 140-142. [data]
D. Zhang, S. Chen, and Z.-H. Zhou. Learning the kernel parameters in kernel minimum distance classifier. Pattern Recognition, 2006, 39(1): 133-135.
X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Face recognition from a single image per person: A survey. Pattern Recognition, 2006, 39(9): 1725-1745. This paper won the Pattern Recognition Journal 2006 Best Paper Award Honorable Mention
X. Geng and Z.-H. Zhou. Image region selection and ensemble for face recognition. Journal of Computer Science and Technology, 2006, 21(1): 116-125. [data]
X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Feature selection for high dimensional face image using self-organizing maps. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, LNAI 3518, 2005, pp.500-504.
D. Zhang, S. Chen, and Z.-H. Zhou. Two-dimensional non-negative matrix factorization for face representation and recognition. In: Proceedings of the 2nd International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'05), in Conjunction with ICCV'05, Beijing, China, LNCS 3723, 2005, pp.350-363. [data]
X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble. IEEE Transactions on Neural Networks, 2005, 16(4): 875-886. [code][data]
X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, 35(6): 1098-1107. [code]
D. Zhang and Z.-H. Zhou. (2D)² PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neurocomputing, 2005, 69(1-3): 224-231. [data]
S. Chen, L. Chen, and Z.-H. Zhou. A unified SWSI-KAMs framework and performance evaluation on face recognition. Neurocomputing, 2005, 68: 54-69. [data]
D. Zhang, S. Chen, and Z.-H. Zhou. A new face recognition method based on SVD perturbation for single example image per person. Applied Mathematics and Computation, 2005, 163(2): 895-907. [data]
X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Robust face recognition from a single training image per person with kernel-based SOM-face. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.858-863. [data]
J. Zhang, H. Shen, and Z.-H. Zhou. Unified locally linear embedding and linear discriminant analysis algorithm (ULLELDA) for face recognition. In: Proceedings of the 5th Chinese Conference on Biometric Recognition (Sinobiometrics'04), Guangzhou, China, LNCS 3338, 2004, pp.296-304.
Z.-H. Zhou and X. Geng. Projection functions for eye detection. Pattern Recognition, 2004, 37(5): 1049-1056.
S. Chen, J. Liu, and Z.-H. Zhou. Making FLDA applicable to face recognition with one sample per person. Pattern Recognition, 2004, 37(7): 1553-1555. [data]
S. Chen, D. Zhang, and Z.-H. Zhou. Enhanced (PC)² A for face recognition with one training image per person. Pattern Recognition Letters, 2004, 25(10): 1173-1181. [data]
J. Wu and Z.-H. Zhou. Efficient face candidates selector for face detection. Pattern Recognition, 2003, 36(5): 1175-1186. [demo]
J. Wu and Z.-H. Zhou. Face recognition with one training image per person. Pattern Recognition Letters, 2002, 23(14): 1711-1719. [data]
F. J. Huang, Z.-H. Zhou, H.-J. Zhang, and T. Chen. Pose invariant face recognition. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), Grenoble, France, 2000, pp.245-250.

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Computer-Aided Medical Diagnosis

M. Li and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 2007, 37(6): 1088-1098. [code]
Y. Jiang, M. Li, and Z.-H. Zhou. Generation of comprehensible hypotheses from gene expression data. In: Proceedings of the International Workshop on Data Mining for Biomedical Application (BioDM'06), in conjunction with PAKDD'06, Singapore, LNBI 3916, 2006, pp.116-123.
N. Li, H.-J. Zhou, J.-J. Ling, and Z.-H. Zhou. Spiculated lesion detection in digital mammogram based on artificial neural network ensemble. In: Proceedings of the 2nd International Symposium on Neural Networks (ISNN'05), Chongqing, China, LNCS 3498, 2005, pp.790-795.
Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42. [code]
Z.-H. Zhou, Y. Jiang, Y.-B. Yang, and S.-F. Chen. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine, 2002, 24(1): 25-36.

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Bioinformatics

S. Ji, L. Yuan, Y.-X. Li, Z.-H. Zhou, S. Kumar, and J. Ye. Drosophila gene expression pattern annotation using sparse features and term-term interactions. In: Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD'09), Paris, France, 2009, pp.407-416.
Y.-X. Li, S. Ji, J. Ye, S. Kumar, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1445-1450. [code]
S. Ji, Y.-X. Li, Z.-H. Zhou, S. Kumar, J. Ye. A bag-of-words approach for drosophila gene expression pattern annotation. BMC Bioinformatics, 2009, 10: 119.
M.-L. Zhang and Z.-H. Zhou. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 1338-1351. [code]
Y. Jiang, M. Li, and Z.-H. Zhou. Generation of comprehensible hypotheses from gene expression data. In: Proceedings of the International Workshop on Data Mining for Biomedical Application (BioDM'06), in conjunction with PAKDD'06, Singapore, LNBI 3916, 2006, pp.116-123.

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Theoretical Aspects of Evolutionary Computation

Y. Yu and Z.-H. Zhou. On the usefulness of infeasible solutions in evolutionary search: A theoretical study. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'08), Hong Kong, China, 2008, pp.835-840.
Y. Yu and Z.-H. Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. Artificial Intelligence, 2008, 172(15): 1809-1832.
Y. Yu and Z.-H. Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.555-560.

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Improving Comprehensibility

Y. Jiang, M. Li, and Z.-H. Zhou. Mining extremely small data sets with application to software reuse. Software: Practice and Experience, 2009, 39(4): 423-440.
Y. Jiang, M. Li, and Z.-H. Zhou. Generation of comprehensible hypotheses from gene expression data. In: Proceedings of the International Workshop on Data Mining for Biomedical Application (BioDM'06), in conjunction with PAKDD'06, Singapore, LNBI 3916, 2006, pp.116-123.
Z.-H. Zhou. Comprehensibility of data mining algorithms. In: J. Wang ed. Encyclopedia of Data Warehousing and Mining, Hershey, PA: IGI, 2005, 190-195.
Z.-H. Zhou and Y. Jiang. NeC4.5: Neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(6): 770-773. [code]
Z.-H. Zhou. Rule extraction: Using neural networks or for neural networks? Journal of Computer Science and Technology, 2004, 19(2): 249-253.
Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42. [code]
Z.-H. Zhou, Y. Jiang, and S.-F. Chen. Extracting symbolic rules from trained neural network ensembles. AI Communications, 2003, 16(1): 3-15.
Z.-H. Zhou and Z.-Q. Chen. Hybrid decision tree. Knowledge-Based Systems, 2002, 15(8): 515-528.

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Miscellaneous

Z.-H. Zhou. Three perspectives of data mining. Artificial Intelligence, 2003, 143(1): 139-146.
Z.-H. Zhou and S.-F. Chen. Evolving fault-tolerant neural networks. Neural Computing & Applications, 2003, 11(3-4): 156-160.
Z.-H. Zhou. Review on Data Mining: Concepts and Techniques. IEEE Transactions on Neural Networks, 2002, 13(5): 1251.
Z.-H. Zhou, S.-F. Chen, and Z.-Q. Chen. Improving tolerance of neural networks against multi-node open fault. In: Proceedings of the INNS-IEEE International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, 2001, vol.3, pp.1687-1692.
Z.-H. Zhou, S. Chen, and Z. Chen. FANNC: A fast adaptive neural network classifier. Knowledge and Information Systems, 2000, 2(1): 115-129. [demo]

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