Subsumption resolution: An efficient and effective technique for semi-naive Bayesian learning

Machine Learning

Volumn 87, Issue 1, 2012, Pages 93-125

  Zheng, Fei ^a   Webb, Geoffrey I ^a   Suraweera, Pramuditha ^a   Zhu, Liguang ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

AODE; Classification; Feature selection; Naive Bayes; Semi naive Bayes

Indexed keywords

AODE; ATTRIBUTE INDEPENDENCE ASSUMPTION; BAYESIAN LEARNING; BAYESIAN TECHNIQUES; CATEGORICAL DATA; CLASSIFICATION TIME; DATA SETS; EXPERIMENTAL COMPARISON; FRIEDMAN TEST; LOGISTIC REGRESSIONS; NAIVE BAYES; SEMI-NAIVE BAYES; TEST TIME; TRAINING TIME; ZERO-ONE;

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; LOGISTICS; REGRESSION ANALYSIS;

CLASSIFIERS;

EID: 84858341634     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-011-5275-2     Document Type: Article

References (48)

1. Cerquides, J., & Mántaras, R. L. D. (2005). Robust Bayesian linear classifier ensembles. In Proceedings of the sixteenth European conference on machine learning (pp. 70-81).
2. Cestnik, B. (1990). Estimating probabilities: a crucial task in machine learning. In Proceedings of the ninth European conference on artificial intelligence (pp. 147-149). London: Pitman.
3. Dash, D., & Cooper, G. F. (2002). Exact model averaging with naive Bayesian classifiers. In Proceedings of the nineteenth international conference on machine learning (pp. 91-98). San Mateo: Morgan Kaufmann.
4. De Raedt, L. (2010a). Logic of generality. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 624-631). New York: Springer.
5. De Raedt, L. D. (2010b). Inductive logic programming. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 529-537). New York: Springer.
6. J. Demšar 2006 Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 1 30 1222.68184 (Pubitemid 43022939)
7. Domingos, P., & Pazzani, M. J. (1996). Beyond independence: conditions for the optimality of the simple Bayesian classifier. In Proceedings of the thirteenth international conference on machine learning (pp. 105-112). San Mateo: Morgan Kaufmann.
8. Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. New York: Wiley.
9. Fayyad, U. M., & Irani, K. B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the thirteenth international joint conference on artificial intelligence (pp. 1022-1029). San Mateo: Morgan Kaufmann.
10. Flores, M., Gámez, J., Martínez, A., & Puerta, J. (2009). GAODE and HAODE: two proposals based on AODE to deal with continuous variables. In Proceedings of the 26th annual international conference on machine learning (pp. 313-320).
11. Frank, E., Hall, M., & Pfahringer, B. (2003). Locally weighted naive Bayes. In Proceedings of the nineteenth conference on uncertainty in artificial intelligence (pp. 249-256). San Mateo: Morgan Kaufmann.
12. M. Friedman 1937 The use of ranks to avoid the assumption of normality implicit in the analysis of variance Journal of the American Statistical Association 32 200 675 701 10.2307/2279372
13. M. Friedman 1940 A comparison of alternative tests of significance for the problem of m rankings Journal of the American Statistical Association 11 1 86 92 0063.01455
14. N. Friedman D. Geiger M. Goldszmidt 1997 Bayesian network classifiers Machine Learning 29 2 131 163 0892.68077 10.1023/A:1007465528199 (Pubitemid 127510036)
15. J. Gama 2003 Iterative Bayes Theoretical Computer Science 292 2 417 430 1961785 1026.68071 10.1016/S0304-3975(02)00179-2
16. D. J. Hand K. Yu 2001 Idiot's Bayes: not so stupid after all? International Statistical Review 69 3 385 398 1213.62010 10.1111/j.1751-5823. 2001.tb00465.x (Pubitemid 33392115)
17. Hastie, T., Tibshirani, R., & Friedman, J. (2001). Elements of statistical learning: data mining, inference and prediction. New York: Springer.
18. Hilden, J., & Bjerregaard, B. (1976). Computer-aided diagnosis and the atypical case. In F. T. de Dombal & F. Gremy (Eds.), Decision making and medical care: can information science help (pp. 365-378). Amsterdam: North-Holland.
19. Iman, R. L., & Davenport, J. M. (1980). Approximations of the critical region of the Friedman statistic. In Communications in statistics (pp. 571-595).
20. Keogh, E. J., & Pazzani, M. J. (1999). Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches. In Proceedings of the international workshop on artificial intelligence and statistics (pp. 225-230).
21. Kittler, J. (1986). Feature selection and extraction. In T. Y. Young & K.-S. Fu (Eds.), Handbook of pattern recognition and image processing. New York: Academic Press.
22. Kohavi, R. (1996). Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid. In Proceedings of the second international conference on knowledge discovery and data mining (pp. 202-207).
23. Kohavi, R., & Wolpert, D. (1996). Bias plus variance decomposition for zero-one loss functions. In Proceedings of the thirteenth international conference on machine learning (pp. 275-283). San Francisco: Morgan Kaufmann.
24. Kononenko, I. (1990). Comparison of inductive and naive Bayesian learning approaches to automatic knowledge acquisition. In B. Wielinga, J. Boose, B. Gaines, G. Schreiber, & M. van Someren (Eds.), Current trends in knowledge acquisition. Amsterdam: IOS Press.
25. Kononenko, I. (1991). Semi-naive Bayesian classifier. In Proceedings of the sixth European working session on machine learning (pp. 206-219). Berlin: Springer.
26. Langley, P. (1993). Induction of recursive Bayesian classifiers. In Proceedings of the 1993 European conference on machine learning (pp. 153-164). Berlin: Springer.
27. Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. In Proceedings of the tenth conference on uncertainty in artificial intelligence (pp. 399-406). San Mateo: Morgan Kaufmann.
28. Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. In Proceedings of the tenth national conference on artificial intelligence (pp. 223-228). Menlo Park: AAAI Press.
29. H. Langseth T. D. Nielsen 2006 Classification using hierarchical naive Bayes models Machine Learning 63 2 135 159 1110.68130 10.1007/s10994-006-6136-2 (1994) (Pubitemid 43951876)
30. Lewis, D. D. (1998). Naive Bayes at forty: the independence assumption in information retrieval. In Proceedings of the tenth European conference on machine learning (pp. 4-15). Berlin: Springer.
31. Mitchell, T. (1997). Machine learning. New York: McGraw Hill.
32. Newman, D., Hettich, S., Blake, C., & Merz, C. (1998). UCI repository of machine learning databases. Irvine: University of California, Department of Information and Computer Science.
33. Pazzani, M. J. (1996). Constructive induction of Cartesian product attributes. In ISIS: information, statistics and induction in science (pp. 66-77).
34. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In Advances in large margin classifiers. Cambridge: MIT Press.
35. Sahami, M. (1996). Learning limited dependence Bayesian classifiers. In Proceedings of the second international conference on knowledge discovery in databases (pp. 334-338). Menlo Park: AAAI Press.
36. G. I. Webb 2000 MultiBoosting: a technique for combining boosting and wagging Machine Learning 40 2 159 196 10.1023/A:1007659514849
37. Webb, G. I., & Pazzani, M. J. (1998). Adjusted probability naive Bayesian induction. In Proceedings of the eleventh Australian joint conference on artificial intelligence (pp. 285-295). Berlin: Springer.
38. G. I. Webb J. Boughton Z. Wang 2005 Not so naive Bayes: aggregating one-dependence estimators Machine Learning 58 1 5 24 1075.68078 10.1007/s10994-005-4258-6 (Pubitemid 40356736)
39. G. I. Webb J. Boughton F. Zheng K. M. Ting H. Salem 2011 Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification Machine Learning 10.1007/s10994-011- 5263-6
40. Witten, I. H., & Frank, E. (2005). Data mining: practical machine learning tools and techniques. San Mateo: Morgan Kaufmann.
41. Zadrozny, B., & Elkan, C. (2001). Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In Proceedings of the eighteenth international conference on machine learning (pp. 609-616). San Francisco: Morgan Kaufmann.
42. Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the eighth international conference on knowledge discovery and data mining (pp. 694-699). New York: ACM Press.
43. N. L. Zhang T. D. Nielsen F. V. Jensen 2004 Latent variable discovery in classification models Artificial Intelligence in Medicine 30 3 283 299 10.1016/j.artmed.2003.11.004 (Pubitemid 38471897)
44. Zhang, H., Jiang, L., & Su, J. (2005). Hidden naive Bayes. In Proceedings of the twentieth national conference on artificial intelligence (pp. 919-924). Menlo Park: AAAI Press. (Pubitemid 43006725)
45. Z. Zheng G. I. Webb 2000 Lazy learning of Bayesian rules Machine Learning 41 1 53 84 10.1023/A:1007613203719
46. Zheng, F., & Webb, G. I. (2005). A comparative study of semi-naive Bayes methods in classification learning. In Proceedings of the fourth Australasian data mining conference (pp. 141-156).
47. Zheng, F., & Webb, G. I. (2006). Efficient lazy elimination for averaged-one dependence estimators. In Proceedings of the twenty-third international conference on machine learning (pp. 1113-1120). New York: ACM Press.
48. Zheng, F., & Webb, G. I. (2007). Finding the right family: parent and child selection for averaged one-dependence estimators. In Proceedings of the eighteenth European conference on machine learning (pp. 490-501). Berlin: Springer.