Handling numeric attributes when comparing Bayesian network classifiers: Does the discretization method matter?

Applied Intelligence

Volumn 34, Issue 3, 2011, Pages 372-385

  Flores, M Julia ^a   Gámez, José A ^a   Martínez, Ana M ^a   Puerta, José M ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

AODE; Bayesian classifiers; Discretization; Naive Bayes

Indexed keywords

AODE; BAYESIAN CLASSIFIER; BAYESIAN METHODS; BAYESIAN NETWORK CLASSIFIERS; BAYESIAN NETWORKS (BNS); CLASSIFIER PERFORMANCE; CONTINUOUS VARIABLES; DATA SETS; DISCRETE VARIABLES; DISCRETIZATION; DISCRETIZATION METHOD; DISCRETIZATIONS; HILL CLIMBING ALGORITHMS; NAIVE BAYES; REAL PROBLEMS; RUNTIMES; SEMI-NAIVE BAYES;

ALGORITHMS; DISCRETE EVENT SIMULATION; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; REAL VARIABLES; SPEECH RECOGNITION; VOLUME MEASUREMENT;

BAYESIAN NETWORKS;

EID: 79956106290     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-011-0286-z     Document Type: Conference Paper

References (30)

1. Asuncion A, Newman DJ (2007) UCI machine learning repository. School of Information and Computer Sciences, University of California, Irvine. http://www.ics.uci.edu/~mlearn/MLRepository.html
2. Bouckaert RR (2005) Bayesian network classifiers in Weka. Technical report
3. W Buntine 1996 A guide to the literature on learning probabilistic networks from data IEEE Trans Knowl Data Eng 8 195 210 10.1109/69.494161 (Pubitemid 126776290)
4. Casella G, Berger R (2001) Statistical inference. Duxbury Resource Center
5. MR Chmielewski J Grzrmala-Busse 1996 Global discretization of continuous attributes as preprocessing for machine learning Int J Approx Reason 15 4 319 331 0949.68560 10.1016/S0888-613X(96)00074-6 (Pubitemid 126414909)
6. C Chow C Liu 1968 Approximating discrete probability distributions with dependence trees IEEE Trans Inf Theory 14 3 462 467 2443718 0165.22305 10.1109/TIT.1968.1054142
7. Collection of Datasets avalaibles from the Weka Official Homepage (2008) http://www.cs.waikato.ac.nz/ml/weka/
8. J Demšar 2006 Statistical comparisons of classifiers over multiple data sets J Mach Learn Res 7 1 30 2274360 (Pubitemid 43022939)
9. Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Proc of the 12th int conf on mach learn, pp 194-202
10. Fayyad UM, Irani KB (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc of the 13th int joint conf on AI, pp 1022-1027
11. Fisher RA (1959) Statistical methods and scientific inference, 2nd edn. Oliver and Boyd, Edinburgh
12. JL Flores I Inza P Larran̈aga 2007 Wrapper discretization by means of estimation of distribution algorithms Intell Data Anal 11 5 525 545
13. Flores MJ, Gámez JA, Martínez AM, Puerta JM (2009) GAODE and HAODE: two proposals based on AODE to deal with continuous variables. In: ICML. ACM int conf proc series, vol 382, p 40
14. N Friedman D Geiger M Goldszmidt 1997 Bayesian network classifiers Mach Learn 29 131 163 0892.68077 10.1023/A:1007465528199 (Pubitemid 127510036)
15. S García F Herrera 2009 An extension on "Statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons J Mach Learn Res 9 2677 2694
16. Hsu C-N, Huang H-J, Wong T-T (2000) Why discretization works for Naive Bayesian classifiers. In: ICML '00: Proc of the 7th int conf on mach learn, San Francisco, CA, USA. Morgan Kaufmann, San Mateo, pp 399-406
17. C-N Hsu H-J Huang T-T Wong 2003 Implications of the Dirichlet assumption for discretization of continuous variables in Naive Bayesian classifiers Mach Learn 53 3 235 263 1089.68096 10.1023/A:1026367023636
18. R Iman J Davenport 1980 Approximations of the critical region of the Friedman statistic Commun Stat, Theory Methods 9 6 571 595 10.1080/ 03610928008827904
19. John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers, pp 338-345
20. Keogh E, Pazzani M (1999) Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches. In: Proc of the 7th int workshop on AI and statistics, pp 225-230
21. Nemenyi PB (1963) Distribution-free multiple comparisons. PhD thesis
22. Sahami M (1996) Learning limited dependence Bayesian classifiers. In: Proc of the 2nd int conf on knowledge discovery in databases, pp 335-338
23. GI Webb 2000 Multiboosting: a technique for combining boosting and wagging Mach Learn 40 2 159 196 10.1023/A:1007659514849
24. Webb GI, Conilione P (2002) Estimating bias and variance from data
25. GI Webb JR Boughton Z Wang 2005 Not so Naive Bayes: aggregating one-dependence estimators Mach Learn 58 1 5 24 1075.68078 10.1007/s10994-005- 4258-6 (Pubitemid 40356736)
26. Weiss NA (2002) Introductory statistics, 6th edn. Greg Tobin
27. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Mateo
28. Y Yang GI Webb 2009 Discretization for Naive-Bayes learning: managing discretization bias and variance Mach Learn 74 1 39 74 10.1007/s10994-008-5083-5
29. Z Zheng GI Webb 2000 Lazy learning of Bayesian rules Mach Learn 41 1 53 84 10.1023/A:1007613203719
30. Zheng F, Webb GI (2005) A comparative study of semi-Naive Bayes methods in classification learning. In: Proc of the 4th Australasian data mining conf (AusDM05), Sydney. University of Technology, Sydney, pp 141-156