FDI in Multivariate Process with Naïve Bayesian Network in the Space of Discriminant Factors - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2006

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.
Fichier principal
Vignette du fichier
tiplica06.pdf (172.51 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00517057 , version 1 (13-09-2010)

Identifiers

  • HAL Id : inria-00517057 , version 1

Cite

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. ⟨inria-00517057⟩

Collections

UNIV-ANGERS
65 View
179 Download

Share

Gmail Facebook X LinkedIn More