Comparative analysis of the impact of discretization on the classification with Naive Bayes and semi-Naïve Bayes classifiers

Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Volumn , Issue , 2008, Pages 823-828

  Mizianty, Marcin ^a,b   Kurgan, Lukasz ^b   Ogiela, Marek ^a  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ROBOT LEARNING; VIDEO AMPLIFIERS;

BAYES CLASSIFIERS; CLASSIFICATION ACCURACIES; CLASSIFICATION MODELS; COMPARATIVE ANALYSIS; CONTINUOUS DATUM; DATA-SETS; DISCRETE DATUM; DISCRETE VALUES; DISCRETIZATION; DISCRETIZATION ALGORITHMS; EMPIRICAL STUDIES; GAUSSIAN KERNELS; MAXIMUM ENTROPIES; NAIVE BAYES; NUMERICAL FEATURES; RUN-TIME; SUPERVISED ALGORITHMS; TRAINING TIME; UNSUPERVISED DISCRETIZATION;

CLASSIFIERS;

EID: 60649104618     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2008.29     Document Type: Conference Paper

References (39)

1. th Conf. Information Technology, pp. 235-236, 2007
2. A. Asuncion, D.J. Newman, UCI Machine Learning Repository, Irvine, CA: Univ. of California, School of Information and Computer Science, 2007 http://www.ics.uci.edu/~mlearn/MLRepository.html
3. M. Boulle, "MODL: A Bayes optimal discretization method for continuous attributes'", Machine Learning, 65 (1), pp. 131-165, 2006
4. J.Y. Ching, A.K. Wong, K.C. Chan, "Class-dependent discretization for inductive learning from continuous and mixed mode data", IEEE Trans. on Pattern Analysis and Machine Intelligence, 17 (7), pp. 641-651, 1995
5. J. Catlett, "On changing continuous attributes into ordered discrete attributes", Proc. European Working Session on Learning, pp. 164-178, 1991
6. K.J. Cios, L Kurgan, "Hybrid inductive machine learning: an overview of clip algorithms", New Learning Paradigms in Soft Computing, L.C. Jain, J. Kacprzyk, ed., pp. 276-322, Physica-Verlag (Springer), 2001
7. K.J. Cios, L Kurgan, "CLIP4: hybrid inductive machine learning algorithm that generates inequality rules", Information Sciences, 163 (1-3), pp. 37-83, 2004
8. P. Clark, T. Niblett, "The CN2 algorithm", Machine Learning, 3, pp. 261-283, 1989
9. th Int. Conf. Machine Learning, pp. 194-202, 1995
10. U. Fayyad, K. Irani, "On the handling of continuous-valued attributes in decision tree generation", Machine Learning, 8, pp. 87-102, 1992
11. th Int. Joint Conf. Artificial Intelligence, pp. 1022-1027, 1993
12. J.L Flores, I. Inza, P. Larrañaga, "Wrapper discretization by means of estimation of distribution algorithms", Intelligent Data Analysis, 11 (5), pp. 525-545, 2007
13. th Int. Conf. Machine Learning and Cybernetics, pp. 3682-3686, 2007
14. W. Huang, "Discretization of continuous attributes for inductive machine learning", M.Sc. thesis, Dept. Computer Science, Univ. of Toledo, Ohio, 1996
15. th Conf. Uncertainty in Artificial Intelligence, pp. 338-345, 1995
16. th Int. Symp. Methodologies for Intelligent Systems, 1999
17. E. Keogh, M. Pazzani, "Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches", Proc. Int. Workshop Artificial Intelligence and Statistics, pp. 225-230, 1999
18. th Int. Conf. Artificial Intelligence, pp. 123-128, 1992
19. th Int. Conf. Knowledge Discovery and Data Mining, pp. 114-119, 1996
20. J. Kujala, T. Elomaa, "Improved algorithms for univariate discretization of continuous features", Proc Principles and Practice of Knowledge Discovery in Databases, LNCS 4702, pp. 188-199, 2007
21. L Kurgan, K.J. Cios, "Discretization algorithm that uses classattribute interdependence maximization," Proc. 2001 Int. Conf. Artif Intelligence, pp. 980-987, 2001
22. L Kurgan, K.J. Cios, "Fast class-attribute interdependence maximization (CAIM) discretization algorithm", Proc. Int. Conf. on Machine Learning and Applications, pp. 30-36, 2003
23. L Kurgan, K.J. Cios, "CAIM discretization algorithm", IEEE Trans. on Knowledge and Data Engineering, 16 (2), pp. 145-153, 2004
24. L Kurgan, K.J. Cios, S. Dick, "Highly scalable and robust rule learner: performance evaluation and comparison", IEEE Trans. on Systems, Man, and Cybernetics, Part B, 36 (1), pp. 32-53, 2006
25. C.-H. Lee, "A Hellinger-based discretization method for numeric attributes in classification learning", Knowledge-Based Systems, 20 (4), pp. 419-425, 2007
26. H. Liu, R Setiono, "Feature selection via discretization", IEEE Trans. on Knowledge and Data Engineering, 9 (4), pp. 642-645, 1997
27. H. Liu, F. Hussain, C. Tan, M. Dash, "Discretization: an enabling technique". J. Data Mining and Knowledge Discovery, 6 (4), pp. 393-423. 2002
28. X. Liu, H. Wang, "A discretization algorithm based on a heterogeneity criterion", IEEE Trans. on Data and Knowledge Engineering, 17 (9), pp. 1166-1173, 2005
29. S. Mehta, S. Parthasarathy, H. Yang, "Toward unsupervised correlation preserving discretization", IEEE Trans. on Knowledge and Data Engineering, 17 (9), pp. 1174-1185, 2005
30. C-T. Su, J-H. Hsu, "An Extended Chi2 algorithm for discretization of real value attributes", IEEE Trans. on Knowledge and Data Engineering, 17 (3), pp. 437-441, 2005
31. F.E.H. Tay, J. Shen, "A modified Chi2 algorithm for discretization", IEEE Trans. on Knowledge and Data Engineering, 14 (3), pp. 666-670, 2002
32. C.-J. Tsai, C.-I. Lee, W.-P. Yang, "A discretization algorithm based on Class-Attribute Contingency Coefficient", Information Sciences, 178, pp. 714-731, 2008
33. K. Wang, B. Liu, "Concurrent discretization of multiple attributes", Pacific-Rim Conf Artificial Intelligence, pp. 250-259, 1998
34. G.I. Webb, J. Boughton, Z. Wang, "Not so naive Bayes: aggregating one-dependence estimators", Machine Learning, 58 (1), pp. 5-24, 2005
35. th edition, Morgan Kaufmann, 2005
36. AK.C. Wong, D.K.Y. Chiu, "Synthesizing statistical knowledge from incomplete mixed-mode data", IEEE Trans. on Pattern Analysis and Machine Intelligence, 9, pp. 796-805, 1987
37. X. Wu, "A Bayesian discretizer for real-valued attributes," Computer Journal, 39 (8), pp. 688-691, 1996
38. X. Wu, V. Kumar, J.R. Quinlan, et al., "Top 10 algorithms in data mining", Knowledge and Information Systems, 14, pp. 1-37, 2008
39. Z. Zheng, G.I. Webb, "Lazy learning of Bayesian rules", Machine Learning, 41 (1), pp. 53-84, 2000