Efficient lazy elimination for averaged one-dependence estimators

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 1113-1120

  Zheng, Fei ^a   Webb, Geoffrey I ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFIERS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; ERROR ANALYSIS; ESTIMATION;

AVERAGED ONE DEPENDENCE ESTIMATORS (AODE); BACKWARDS SEQUENTIAL ELIMINATION (BSE); BAYESIAN CLASSIFIERS; LAZY ELIMINATION (LE);

PROBLEM SOLVING;

EID: 33749265151     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Cerquides, J., & Mántaras, R. L. D. (2005). Robust Bayesian linear classifier ensembles. Proc. 16th European Conf. Machine Learning, Lecture Notes in Computer Science (pp. 70-81).
2. Domingos, P., & Pazzani, M. J. (1996). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Proc. 13th Int. Conf. Machine Learning (pp. 105-112). Morgan Kaufmann.
3. Duda, R. O., & Hart, P. E. (1973). Pattern Classification and Scene Analysis. New York: John Wiley and Sons.
4. Fayyad, U. M., & Irani, K. B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. Proc. 13th Int. Joint Conf. Artificial Intelligence (IJCAI-93) (pp. 1022-1029). Morgan Kaufmann.
5. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131-163.
6. Hastie, T., Tibshirani, R., & Friedman, J. (2001). Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.
7. Keogh, E. J., & Pazzani, M. J. (1999). Learning augmented Bayesian classifers: A comparison of distribution-based and classification-based approaches. Proc. Int. Workshop on Artificial Intelligence and Statistics (pp. 225-230).
8. Kittler, J. (1986). Feature selection and extraction. In T. Y. Young and K.-S. Fu (Eds.), Handbook of Pattern Recognition and Image Processing. New York: Academic Press.
9. Kohavi, R. (1996). Scaling up the accuracy of naiveBayes classifiers: a decision-tree hybrid. Proc. 2nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (pp. 202-207).
10. Kohavi, R., & Wolpert, D. (1996). Bias plus variance decomposition for zero-one loss functions. Proc. 13th Int. Conf. Machine Learning (pp. 275-283). San Francisco: Morgan Kaufmann.
11. Langley, P. (1993). Induction of recursive Bayesian classifiers. Proc. 1993 European Conf. Machine Learning (pp. 153-164). Berlin: Springer-Verlag.
12. Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. Proc. 10th Conf. Uncertainty in Artificial Intelligence (pp. 399-406). Morgan Kaufmann.
13. Pazzani, M. J. (1996). Constructive induction of Cartesian product attributes. ISIS: Information, Statistics and Induction in Science, 66-77.
14. Sahami, M. (1996). Learning limited dependence Bayesian classifiers. Proc. 2nd Int. Conf. Knowledge Discovery in Databases (pp. 334-338). Menlo Park, CA: AAAI Press.
15. Webb, G. I. (2000). Multiboosting: A technique for combining boosting and wagging. Machine Learning, 40, 159-196.
16. Webb, G. I. (2001). Candidate elimination criteria for lazy Bayesian rules. Proc. 14th Australian Joint Conf. Artificial Intelligence (pp. 545-556). Berlin: Springer.
17. Webb, G. I., Boughton, J., & Wang, Z. (2005). Not so naive Bayes: Aggregating one-dependence estimators. Machine Learning, 58, 5-24.
18. Webb, G. I., & Pazzani, M. J. (1998). Adjusted probability naive Bayesian induction. Proc. 11th Australian Joint Conf. Artificial Intelligence (pp. 285-295). Berlin:Springer.
19. Witten, I. H., & Frank, E. (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA: Morgan Kaufmann.
20. Yang, Y., Korb, K., Ting, K.-M., & Webb, G. I. (2005). Ensemble selection for superparent-one-dependence estimators. Proc. 18th Australian Joint Conf. Artificial Intelligence (pp. 102-111). Springer.
21. Zhang, H., Jiang, L., & Su, J. (2005). Hidden naive Bayes. Proc. 20th National Conf. Artificial Intelligence (AAAI 2005) (pp. 919-924). AAAI Press.
22. Zheng, F., & Webb, G. I. (2005). A comparative study of semi-naive Bayes methods in classification learning. Proc. 4th Australasian Data Mining conference (AusDM05) (pp. 141-156).
23. Zheng, Z., & Webb, G. I. (2000). Lazy learning of Bayesian rules. Machine Learning, 41, 53-84.