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A binary classification methodology applicable to defects detection.Boosting algorithms

Published online by Cambridge University Press:  15 October 2000

I. Marie-Joseph ,
A. Oukaour ,
H. Clergeot  and
A. Primerose
I. Marie-Joseph*
Affiliation:
Laboratoire de Traitement du Signal et de Modélisation des Machines, Institut d'Études Supérieures de la Guyane, avenue d'Estrée, BP 792, 97337 Cayenne Cedex, France
A. Oukaour
Affiliation:
Laboratoire de Traitement du Signal et de Modélisation des Machines, Institut d'Études Supérieures de la Guyane, avenue d'Estrée, BP 792, 97337 Cayenne Cedex, France
H. Clergeot
Affiliation:
Laboratoire de Traitement du Signal et de Modélisation des Machines, Institut d'Études Supérieures de la Guyane, avenue d'Estrée, BP 792, 97337 Cayenne Cedex, France
A. Primerose
Affiliation:
Laboratoire de Traitement du Signal et de Modélisation des Machines, Institut d'Études Supérieures de la Guyane, avenue d'Estrée, BP 792, 97337 Cayenne Cedex, France
*
isabelle.mariejoseph@guyane.univ-ag.fr
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Abstract

This article presents a binary classification method which is used in defects detection. It's presented as recursives “boosting” algorithms which allow us to obtain a precise discriminating function by combination of hypothesis and rules with moderate accuracy. This approach permits the study of random phenomena governed by nonparametric laws and a direct decision for the observations classification and the determination of frontiers in an observation space. The various analyses which will be developed are illustrated by simulations making it possible to evaluate the possibilities of the method.

Keywords

Type
Research Article
Information
The European Physical Journal - Applied Physics , Volume 12 , Issue 1 , October 2000 , pp. 61 - 74
Copyright
© EDP Sciences, 2000

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References

B. Alachkar, Diagnostic vibro-acoustique des défauts de fabrication des machines électriques, thèse de Doctorat de l'Université de Paris-Sud, Centre d'Orsay, 1995.
B. Dubuisson, Diagnostic et reconnaissance des formes (Éditions Hermès, 1990).
Y. Freund, R.E. Schapire, Experiments with a New Boosting Algorithm, in Machine Learning: Proceedings of the Thirteenth International Conference, 1996, pp. 148-156.
R.E. Schapire, Theorical views of boosting, in Computational Learning Theory: Fourth European Conference, EuroCOLT'99, 1999.
R.E. Schapire, Y. Singer, Improved boosting algorithms using confidence-rated predictions, in Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 1998.
G. Zwingelstein, Diagnostic des défaillances - Théorie et pratique pour les systèmes industriels (Éditions Hermès, 1995).