To select or to weigh: A comparative study of model selection and model weighing for SPODE ensembles

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4212 LNAI, Issue , 2006, Pages 533-544

  Yang, Ying ^a   Webb, Geoff ^a   Cerquides, Jesus ^b   Korb, Kevin ^a   Boughton, Janice ^a   Ting, Kai Ming ^a  

^a MONASH UNIVERSITY   (Australia)

^b UNIVERSITY OF BARCELONA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); ERROR ANALYSIS; MATHEMATICAL MODELS; PROBABILITY;

BAYES CLASSIFIERS; CLASSIFICATION ACCURACY; MODEL SELECTION; PROBABILITY ESTIMATES;

PARAMETER ESTIMATION;

EID: 33750313997     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_50     Document Type: Conference Paper

References (38)

1. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2) (1997) 131-163
2. Keogh, E.J., Pazzani, M.J.: Learning augmented Bayesian classifers: A comparison of distribution-based and classification-based approaches. In: Proceedings of the International Workshop on Artificial Intelligence and Statistics. (1999) 225-230
3. Keogh, E.J., Pazzani, M.J.: Learning the structure of augmented Bayesian classifiers. International Journal on Artificial Intelligence Tools 11(40) (2002) 587-601
4. Kittler, J.: Feature selection and extraction. In Young, T.Y., Fu, K.S., eds.: Handbook of Pattern Recognition and Image Processing, New York (1986)
5. Kohavi, R.: Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid. In: Proceedings of the 2nd SIGKDD. (1996) 202-207
6. Kononenko, I.: Semi-naive Bayesian classifier. In: Proceedings of the 6th European Working Session on Machine learning. (1991) 206-219
7. Langley, P.: Induction of recursive Bayesian classifiers. In: Proceedings of the 4th ECML. (1993) 153-164
8. Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Proceedings of the 10th UAL (1994) 399-406
9. Pazzani, M.J.: Constructive induction of Cartesian product attributes. ISIS: Information, Statistics and Induction in Science (1996) 66-77
10. Sahami, M.: Learning limited dependence Bayesian classifiers. In: Proceedings of the 2nd SIGKDD. (1996) 334-338
11. Singh, M., Provan, G.M.: Efficient learning of selective Bayesian network classifiers. In: Proceedings of the 13th ICML. (1996) 453-461
12. Webb, G.I.: Candidate elimination criteria for lazy Bayesian rules. In: Proceedings of the 14th Australian AI. (2001) 545-556
13. Webb, G.I., Boughton, J., Wang, Z.: Not so naive Bayes: Aggregating one-dependence estimators. Machine Learning 58(1) (2005) 5-24
14. Webb, G.I., Pazzani, M.J.: Adjusted probability naive Bayesian induction. In: Proceedings of the 11th Australian AI. (1998) 285-295
15. Xie, Z., Hsu, W., Liu, Z., Lee, M.L.: Snnb: A selective neighborhood based naive Bayes for lazy learning. In: Proceedings of the 6th PAKDD. (2002) 104-114
16. Zheng, Z., Webb, G.I.: Lazy learning of Bayesian rules. Machine Learning 41(1) (2000) 53-84
17. Zheng, Z., Webb, G.I., Ting, K.M.: Lazy Bayesian rules: A lazy semi-naive Bayesian learning technique competitive to boosting decision trees. In: Proceedings of the 16th ICML. (1999) 493-502
18. Cerquides, J., de Mántaras, R.L.: Robust bayesian linear classifier ensembles. In: Proceedings of the 16th ECML. (2005) 72-83
19. De Ferrari, L.: Mining housekeeping genes with a naive Bayes classifier. MSc Thesis, University of Edinburgh, School of Informatics (2005)
20. Flikka, K., Martens, L., Vandekerckhove, J., Gevaert, K., Eidhammeri, I.: Improving throughput and reliability of peptide identifications through spectrum quality evaluation. In: Proceedings of the 9th Annual International Conference on Research in Computational Molecular Biology. (2005)
21. Nikora, A.P.: Classifying requirements: Towards a more rigorous analysis of natural-language specifications. In: Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering. (2005) 291-300
22. Yang, Y., Korb, K., Ting, K.M., Webb, G.I.: Ensemble selection for superparent-one-dependence estimators. In: Proceedings of the 18th Australian AI. (2005) 102-112
23. Zheng, F., Webb, G.I.: Efficient lazy elimination for averaged one-dependence estimators. In: Proceedings of the 23rd ICML. (2006)
24. Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27(3) (1948) 379-423
25. Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-5
26. Korb, K., Nicholson, A.: Bayesian Artificial Intelligence. Chapman & Hall/CRC, Boca Raton, FL (2004)
27. Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T.: Bayesian model averaging: A tutorial. Statistical Science 14(4) (1999) 382417
28. Cooper, G. F., Herskovits, E.: A Bayesian method for constructing Bayesian belief networks from databases. In: Proceedings of the 7th UAI. (1991) 86-94
29. Pedregal, P.: Introduction to Optimization. Number 46 in Texts in Applied Mathematics. Springer (2004)
30. Heath, M.T.: Scientific Computing: An Introductory Survey, 2nd Edition. McGraw-Hill, New York (2002)
31. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases [http://www.ics.uci.edu/~mlearn/mlrepository.html] (1998) Department of Information and Computer Science, University of California, Irvine.
32. Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th IJCAI. (1993) 1022-1027
33. Breiman, L.: Bias, variance and arcing classifiers, technical report 460, Statistics Department, University of California, Berkeley (1996)
34. Friedman, J.H.: On bias, variance, 0/1-loss, and the curse-of- dimensionality. Data Mining and Knowledge Discovery 1(1) (1997) 55-77
35. Kohavi, R., Wolpert, D.: Bias plus variance decomposition for zero-one loss functions. In: Proceedings of the 13th ICML. (1996) 275-283
36. Kong, E.B., Dietterich, T.G.: Error-correcting output coding corrects bias and variance. In: Proceedings of the 12th ICML. (1995) 313-321
37. Webb, G.I.: Multiboosting: A technique for combining boosting and wagging. Machine Learning 40(2) (2000) 159-196
38. Moore, D.S., McCabe, G.P.: Introduction to the Practice of Statistics, Fourth Edition. Michelle Julet (2002)