Structure identification of Bayesian classifiers based on GMDH

Knowledge-Based Systems

Volumn 22, Issue 6, 2009, Pages 461-470

  Xiao, Jin ^a   He, Changzheng ^a   Jiang, Xiaoyi ^b  

^a SICHUAN UNIVERSITY   (China)

^b UNIVERSITY OF MÜNSTER   (Germany)

Author keywords

Bayesian network; Classification; GMBC; GMDH; Structure identification

Indexed keywords

ADAPTIVE STRUCTURE; BAYESIAN CLASSIFICATION; BAYESIAN CLASSIFIER; CLASSIFICATION; DATA SETS; DEPENDENCE ANALYSIS; GMBC; GMDH; PRIOR INFORMATION; STRUCTURE IDENTIFICATION;

CLASSIFIERS; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING SYSTEMS; SET THEORY; STATISTICAL TESTS; STRUCTURE (COMPOSITION);

BAYESIAN NETWORKS;

EID: 67650498428     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2009.06.005     Document Type: Article

References (43)

1. Abdel-Aal R.E., Elhadidy M.A., and Shaahid S.M. Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. Renewable Energy 34 7 (2008) 1686-1699
2. Akaike H. A new look at the statistical identification model. IEEE Transactions on Automatic Control 19 (1974) 716-723
3. Allen D., and Darwiche A. RC_Link: genetic linkage analysis using Bayesian networks. International Journal of Approximate Reasoning 48 2 (2008) 499-525
4. Astrom K.J., and Eykhoff P. System identification, a survey. Automatica 7 (1971) 123-162
5. Baumgartner K., Ferrari S., and Palermo G. Constructing Bayesian networks for criminal profiling from limited data. Knowledge-Based Systems 21 (2008) 563-572
6. Cheng J., Bell D., and Liu W. Learning belief networks from data: an information theory based approach. Artificial Intelligence 137 1-2 (2002) 43-90
7. J. Cheng, R. Greiner, Comparing Bayesian network classifiers, in: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999, pp. 101-108.
8. Chickering D.M. Learning Bayesian networks is NP-complete. In: Fisher D., and Lenz H. (Eds). Learning from Data: Artificial Intelligence and Statistics vol. V (1996), Springer-Verlag 121-130
9. Cooper G.F., and Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (1992) 309-347
10. Demsar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
11. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning 40 2 (2000) 139-157
12. Duda R.O., and Hart P.E. Pattern Classification and Scene Analysis (1973), John Wiley & Sons
13. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of 13th International Joint Conference on Artificial Intelligence, 1993, pp. 1022-1027.
14. Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32 (1937) 675-701
15. Friedman N., Geiger D., and Goldszmidt M. Bayesian network classifiers. Machine Learning 29 (1997) 131-163
16. N. Friedman, M. Goldszmidt, Building classifiers using Bayesian network, in: Proceedings AAAI-96, Thirteenth National Conference on Artificial Intelligence, 1996, pp. 1227-1284.
17. He C.Z. Self-Organizing Data Mining and Economic Forecasting (2005), Science Publish, Beijing
18. Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (1979) 65-70
19. Huang C., and Darwiche A. Inference in belief networks: a procedural guide. International Journal of Approximate Reasoning 3 (1996) 225-263
20. Iman R.L., and Davenport J.M. Approximations of the critical region of the Friedman statistic. Communications in Statistics Part A - Theory Methods 9 (1980) 571-595
21. Ivakhnenko A.G. Heuristic self-organization in problems of engineering cybernetics. Automatica 6 2 (1970) 207-219
22. Ivakhnenko A.G. The group method of data handling in prediction problems. Soviet Automatic Control c/c of Avtomatika 9 6 (1976) 21-30
23. Ivakhnenko A.G., and Stepasko V.S. Noise Immunity for Modeling (1985), Naukova dumka, Kiev
24. Kohavi R., and John G. Wrappers for feature subset selection. Artificial Intelligence 97 (1997) 273-324
25. Kupinski M.A., Edwards D.C., Giger M.L., and Metz C.E. Ideal observer approximation using Bayesian classification neural networks. IEEE Transactions on Medical Imaging 20 9 (2001) 886-899
26. P. Langley, S. Sage, Induction of selective Bayesian classifiers, in: Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, 1994, pp. 399-406.
27. J.G. Li, C.S. Zhang, T. Wang, Y.M. Zhang, Generalized additive Bayesian network classifiers, in: International Joint Conference on Artificial Intelligence, 2007, pp. 913-918.
28. Madala H.R., and Ivakhnenko A.G. Inductive Learning Algorithms for Complex Systems Modeling (1994), CRC Press, Boca Raton, FL
29. Mehrara M., Moeini A., Ahrari M., and Erfanifard A. Investigating the efficiency in oil futures market based on GMDH approach. Expert Systems with Applications 36 4 (2009) 7479-7483
30. C. Merz, P. Murphy, UCI Machine Learning Repository, University of California, Irvine, 2007. .
31. Muller J.A., and Lemke F. Self-Organising Data Mining: An Intelligent Approach to Extract Knowledge from Data (2000), Hamburg, Libri
32. Neapolitan R.E. Learning Bayesian Networks (2003), Prentice Hall, Upper Saddle River, NJ, New York
33. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan Kaufman
34. Pedrycz W., and Reformat M. Evolutionary optimization of fuzzy models in fuzzy logic: a framework for the new millennium. In: Dimitrov V., and Korotkich V. (Eds). Studies in Fuzziness and Soft Computing vol. 8 (1996), Physica-Verlag, Wurzburg 51-67
35. Perez A., Larranaga P., and Inza I. Supervised classification with conditional Gaussian networks: increasing the structure complexity from naive Bayes. International Journal of Approximate Reasoning 43 (2006) 1-25
36. Puig V., Witczak M., Nejjari F., Quevedo J., and Korbicz J. A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test. Engineering Applications of Artificial Intelligence 20 (2007) 886-897
37. Sanchez E., Shibata T., and Zadeh L.A. Genetic Algorithms and Fuzzy Logic Systems (1997), World Scientific, Singapore
38. Sarychev A.P. An averaged regularity criterion for the group method of data handling in the problem of searching for the best regression. Soviet Journal of Automation and Information Sciences c/c of Avtomatika 23 5 (1990) 24-29
39. Schwarz G. Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-464
40. Sheskin D. Handbook of Parametric and Nonparametric Statistical Procedures. second ed. (2006), Chapman and Hall/CRC
41. A.P. Alves da Silva, U.P. Rodrigues, A.J. Rocha Reis, L.S. Moulin, NeuroDem-a neural network based short term demand forecaster, in: IEEE Power Tech Proceedings, Porto, 2001.
42. Tassadduq I., Rehman S., and Bubshait K. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renewable Energy 25 (2002) 545-554
43. Zhu X.Q., and Wu X.D. Class noise vs. attribute noise: a quantitative study of their impacts. Artificial Intelligence Review 22 (2004) 177-210