FDI in Multivariate Process with Naïve Bayesian Network in the Space of Discriminant Factors

Abstract : The Naïve Bayesian Network (NBN) classifier is an optimal classifier(in the sense of minimal classification error rate) in the case of independent descriptors or variables. The presence of dependencies between variables generally reduce his efficiency. In this article, we are proposing a new classification method named Naïve Bayesian Network in the Space of Discriminants Factors (NBNSDF) which is based on the use of the NBN in the space of discriminants factors issue from a discriminant analysis. The discriminants factors are not correlated letting very efficient the use of the NBN. We found on simulated data that the NBNSDF method better detects and isolates faults in multivariate processes than the NBN in the case of strongly correlated variables.
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Communication dans un congrès
International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA'06), 2006, Sydney, Australia. 2006
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https://hal.inria.fr/inria-00517057
Contributeur : Sylvain Verron <>
Soumis le : lundi 13 septembre 2010 - 14:27:02
Dernière modification le : lundi 13 septembre 2010 - 20:52:35
Document(s) archivé(s) le : jeudi 30 juin 2011 - 13:27:28

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  • HAL Id : inria-00517057, version 1

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Teodor Tiplica, Sylvain Verron, Abdessamad Kobi, Iulian Nastac. FDI in Multivariate Process with Naïve Bayesian Network in the Space of Discriminant Factors. International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA'06), 2006, Sydney, Australia. 2006. 〈inria-00517057〉

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