18.Hui, B., Y. Yang, and G.I. Webb (2009). Anytime Classification for a Pool of Instances. Machine Learning 77(1). Netherlands: Springer, pages 61-102. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
17.Yang, Y. and G.I. Webb (2009). Discretization for Naive-Bayes Learning: Managing Discretization Bias and Variance. Machine Learning 74(1). Netherlands: Springer, pages 39-74. [Abstract] [Pre-Publication PDF][Link to paper via Springerlink]
16.Yang, Y., G.I. Webb, K. Korb, and K-M. Ting (2007). Classifying under Computational Resource Constraints: Anytime Classification Using Probabilistic Estimators. Machine Learning 69(1). Netherlands: Springer, pages 35-53. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
15.Yang, Y., G.I. Webb, J. Cerquides, K. Korb, J. Boughton, and K-M. Ting (2007). To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators. IEEE Transactions on Knowledge and Data Engineering (TKDE) 19(12). Los Alamitos, CA: IEEE Computer Society, pages 1652-1665. [Abstract] [Pre-publication PDF][ Link to paper via IEEE]
14.Zheng, F. and G.I. Webb (2007). Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators. In Lecture Notes in Artificial Intelligence 4710: Proceedings of the 18th European Conference on Machine Learning (ECML'07) Warsaw, Poland. Berlin/Heidelberg: Springer-Verlag, pages 490-501. [Abstract] [Pre-publication PDF]
13.Zheng, F. and G.I. Webb (2006). Efficient Lazy Elimination for Averaged One-Dependence Estimators. In W. Cohen and A. Moore (Eds.), ACM International Conference Proceeding Series, Vol. 148: The Proceedings of the Twenty-third International Conference on Machine Learning (ICML'06) Pittsburgh, Pennsylvania. New York, NY: ACM Press, pages 1113 - 1120. [Abstract] [Pre-publication PDF][Link to paper via ACM Portal]
12.Yang, Y., G.I. Webb, J. Cerquides, K. Korb, J. Boughton, and K-M. Ting (2006). To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. In J. Furkranz, T. Scheffer and M. Spiliopoulou (Eds.), Lecture Notes in Computer Science 4212: Proceedings of the 17th European Conference on Machine Learning (ECML'06) Berlin, Germany. Berlin/Heidelberg: Springer-Verlag, pages 533-544. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
11.Webb, G. I., J. Boughton, and Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning 58(1). Netherlands: Springer, pages 5-24. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
10.Zheng, F. and G.I. Webb (2005). A Comparative Study of Semi-naive Bayes Methods in Classification Learning. In S.J. Simoff, G.J. Williams, J. Galloway and I. Kolyshkina (Eds.), Proceedings of the Fourth Australasian Data Mining Conference (AusDM05) Sydney, Australia. Sydney: University of Technology, pages 141-156. [Abstract] [Pre-publication PDF]
9.Shi, H., Z. Wang, G.I. Webb, and H. Huang (2003). A New Restricted Bayesian Network Classifier. In K-Y. Whang, J. Jeon, K. Shim and J. Srivastava (Eds.), Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03) Seoul, Korea. Berlin/Heidelberg: Springer-Verlag, pages 265-270. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
8.Wang, Z., G.I. Webb, and F. Zheng (2003). Adjusting Dependence Relations for Semi-Lazy TAN Classifiers. In T.D. Gedeon and L.C.C. Fung (Eds.), Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03) Perth, Australia. Berlin/Heidelberg: Springer, pages 453-465. [Abstract] [Pre-publication PDF ][Link to paper via Springerlink]
7.Wang, Z. and G.I. Webb (2002). Comparison of Lazy Bayesian Rule Learning and Tree-Augmented Bayesian Learning. In Proceedings of the IEEE International Conference on Data Mining (ICDM-2002) Maebashi City, Japan. Los Alamitos, CA: IEEE Computer Society, pages 775-778. [Abstract] [PDF][Link to paper via IEEE xplore]
6.Wang, Z. and G. I. Webb (2002). A Heuristic Lazy Bayesian Rules Algorithm. In S.J Simoff, G.J Williams and M. Hegland (Eds.), Proceedings of the First Australasian Data Mining Workshop (AusDM02) Canberra, Australia. Sydney: University of Technology, pages 57-63. [Abstract] [PDF]
5.Webb, G. I., J. Boughton, and Z. Wang (2002). Averaged One-Dependence Estimators: Preliminary Results. In S.J Simoff, G.J Williams and M. Hegland (Eds.), Proceedings of the First Australasian Data Mining Workshop (AusDM02) Canberra, Australia. Sydney: University of Technology, pages 65-73. [Abstract] [PDF]
4.Wang, Z., G. I. Webb, and H. Dai (2001). Implementation of Lazy Bayesian Rules in the Weka System. In Software Technology Catering for 21st Century: Proceedings of the International Symposium on Future Software Technology (ISFST2001) Zheng Zhou, China. Tokyo: Software Engineers Association, pages 204-208. [Abstract]
3.Zheng, Z. and G. I. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning 41(1). Netherlands: Springer, pages 53-84. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
2.Zheng, Z., G. I. Webb, and K. M. Ting (1999). Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. In I. Bratko and S. Dzeroski (Eds.), Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99) Bled, Slovenia. San Francisco: Morgan Kaufmann, pages 493-502. [Abstract] [Pre-publication PDF]
1.Ting, K.M. and Z. Zheng, & G. I. Webb (1999). Learning Lazy Rules to Improve the Performance of Classifiers. In F. Coenen and A. Macintosh (Eds.), Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (ES'99) Peterhouse College, Cambridge, UK. New York: Springer, pages 122-131. [Abstract] [Pre-publication PDF]