Supervised classification using probabilistic decision graphs

Computational Statistics and Data Analysis

Volumn 53, Issue 4, 2009, Pages 1299-1311

  Nielsen, Jens D ^a   Rumí, Rafael ^b   Salmerón, Antonio ^b  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

^b UNIVERSITY OF ALMERÍA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

FOURIER TRANSFORMS; GRAPHIC METHODS; SPEECH RECOGNITION; TREES (MATHEMATICS);

BAYESIAN CLASSIFIERS; CLASSIFICATIO N PROBLEMS; DATA SETS; DECISION GRAPHS; NEW MODEL; REAL WORLD; SUPERVISED CLASSIFICATION; WIDE-RANGE;

GRAPH THEORY;

EID: 58549096270     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.11.003     Document Type: Article

References (28)

1. Abellán J., and Moral S. Building classification trees using the total uncertainty criterion. International Journal of Intelligent Systems 18 (2003) 1215-1225
2. Boutilier C., Friedman N., Goldszmidt M., and Koller D. Context-specific independence in Bayesian networks. In: Horvitz E., and Jensen F. (Eds). Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (1996), Morgan & Kaufmann 115-123
3. Bozga M., and Maler O. On the Representation of Probabilities over Structured Domains. Proceedings of the 11th International Conference on Computer Aided Verification (1999), Springer 261-273
4. Breiman L., Friedman J., Stone C.J., and Olshen R. Classification and Regression Trees (1984), Chapman & Hall
5. Bryant R.E. Symbolic boolean manipulation with ordered binary decision diagrams. ACM Computing Surveys 24 3 (1992) 293-318
6. Castillo E., Gutiérrez J.M., and Hadi A.S. Expert Systems and Probabilistic Network Models (1997), Springer-Verlag
7. Cheeseman P., and Stutz J. Bayesian Classification (AutoClass): Theory and Results. Advances in Knowledge Discovery and Data Mining (1996), AAAI Press 153-180
8. Chow C.K., and Liu C.N. Approximating discrete probability distributions with dependence trees. IEE Transactions on Information Theory 14 3 (1968) 462-467
9. Cutillo L., and Amato U. Localized empirical discriminant analysis. Computational Statistics & Data Analysis 52 (2008) 4966-4978
10. Demšar J. Statistical comparison of classifiers over multiple datasets. Journal of Machine Learning Research 7 (2006) 1-30
11. Domingos P., and Pazzani M.J. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning 29 2-3 (1997) 103-130
12. Friedman N., Geiger D., and Goldszmidt M. Bayesian network classifiers. Machine Learning 29 (1997) 131-163
13. Jaeger M. Probabilistic classifiers and the concepts they recognize. Proceedings of the Twentieth International Conference on Machine Learning (2003), AAAI Press 266-273
14. Jaeger M. Probabilistic decision graphs-combining verification and AI techniques for probabilistic inference. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12 (2004) 19-42
15. Jaeger, M., Nielsen, J.D., Silander, T., 2004. Learning probabilistic decision graphs. In: Proceedings of 2nd European Workshop on Probabilistic Graphical Models. pp. 113-120
16. Jaeger M., Nielsen J.D., and Silander T. Learning probabilistic decision graphs. International Journal of Approximate Reasoning 42 1-2 (2006) 84-100
17. Jensen F.V. Bayesian Networks and Decision Graphs (2001), Springer
18. Jensen F.V., Lauritzen S.L., and Olesen K.G. Bayesian updating in causal probabilistic networks by local computation. Computational Statistics Quarterly 4 (1990) 269-282
19. Lucas, P.J., 2002. Restricted bayesian network structure learning. In: Gámez, J., Salmerón, A. (Eds.), Proceedings of the 1st European Workshop on Probabilistic Graphical Models (PGM'02). pp. 117-126
20. Minsky M. Steps toward artificial intelligence. In: Feigenbaum E.A., and Feldman J. (Eds). Computers and Thoughts (1963), McGraw-Hill 406-450
21. Newman, D., Hettich, S., Blake, C., Merz, C., 1998. UCI repository of machine learning databases http://www.ics.uci.edu/~mlearn/MLRepository.html
22. Ouali A., Cherif A.R., and Krebs M.O. Data mining based bayesian networks for best classification. Computational Statistics & Data Analysis 51 (2006) 1278-1292
23. Park C., Koo J.-Y., Kim S., Sohn I., and Lee J.W. Classification of gene functions using support vector machine for time-course gene expression data. Computational Statistics & Data Analysis 52 (2008) 2578-2587
24. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan Kaufmann Publishers
25. Quinlan J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
26. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann Publishers
27. Sahami, M., 1996. Learning limited dependence bayesian classifiers. In: KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. pp.335-338
28. The Elvira Consortium,, 2002. Elvira: An environment for probabilistic graphical models. In: Gámez, J., Salmerón, A. (Eds.), Proceedings of the First European Workshop on Probabilistic Graphical Models. pp. 222-230