FDI in multivariate process with naïve Bayesian network in the space of discriminant factors

CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ...

Volumn , Issue , 2006, Pages 216-

  Tiplica, Teodor ^a   Verron, Sylvain ^a   Kobi, Abdessamad ^a   Nastac, Iulian ^b  

^a UNIVERSITÉ D'ANGERS   (France)

^b CAQ Laboratory of Electronic and Telecommunications   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; CORRELATION METHODS; DATA REDUCTION; DISCRIMINANT ANALYSIS;

DISCRIMINANTS FACTORS; INDEPENDENT DESCRIPTORS; OPTIMAL CLASSIFIERS;

BAYESIAN NETWORKS;

EID: 38849154328     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIMCA.2006.97     Document Type: Conference Paper

References (18)

1. B. R. Bakshi. "Multiscale pea with application to multivariate statistical process monitoring". AIChE Journal, 44(7): 1596. 1610, 1998.
2. S. D. Bay, "Multivariate discretization of continuous variables for set mining". In KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000.
3. U. Fayyad, O. Piatetsky-Shapiro, and P. Smyth. "From data mining to knowledge discovery in databases". Al Magazine, 17(3):37.53, 1996.
4. N. Friedman, D. Geiger, and M. Goldszmidt. "Bayesian network classifiers". Machine Learning, 29(2-3): 131.163, 1997.
5. Hornik, K., Stinchcombe. M. and White H. "Multilayer feedforward networks are universal approximators", Neural Networks, 2:359.366, 1989.
6. I. Inza, P. Larranaga, B. Sierra, R. Etxeberria, J. Lozano, and J. Pena. "Representing the behaviour of supervised classification learning algorithms by bayesian networks". Pattern Recognition Letters, 20(11-13):1201.1209, 1999.
7. G. H. John, R. Kohavi, and K. Pfleger. "Irrelevant features and the subset selection problem". In International Conference on Machine Learning, pages 121.129, 1994.
8. R. Kohavi, D. Sommcrfield, and J. Dougherty. "Data mining using mlc++. a machine learning library in c++". International Journal on Artificial Intelligence Tools, 6(4):234.245. 1997.
9. P. Langlcy, W. Iba, and K. Thompson. "An analysis of bayesian classifiers". In National Conference on Artificial Intelligence, 1992.
10. P. Langley and S. Sage. "Induction of selective bayesian classifiers". In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 1994.
11. L. Lebart, A. Morineau, and M. Piron. Statistique exploratoire muttidimensiotmelle. DUNOD, 2000.
12. M. G. Madden. " The performance of bayesian network classifiers constructed using different techniques". In Proceedings of European Conference on Machine Learning, Workshop on Probabilistic Graphical Models for Classification, September 2003.
13. M. Moller. "A scaled conjugate gradient algorithm for fast supervised learning". Neural Networks, 6:525.533, 1993.
14. S. Monti and G. Cooper. "A multivariate discretization method for learning bayesian networks from mixed data". In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, 1998.
15. O'Leary, D.E. (1998), "Using Neural Networks to Predict Corporate Failure", International Journal of Intelligent Systems in Accounting, Finance. & Management 7:187.197, 1998.
16. C. Schaffer. "A conservation law for generalisation performance". In Proceedings of the Twelfth International Conference on Machine Learning, pages 259.265, 1994.
17. D. Wolpert. "The relationship between pac, the statistical physics framework, the bayesian framework and the vc framework." 1994.
18. Y. Yang and G. I. Webb. "A comparative study of discretization methods for naive-bayes classifiers", In Proceedings of PKAW 2002, 2002