Authors:
Ibtissem Ben Othman
;
Wissal Drira
;
Faycel El Ayeb
and
Faouzi Ghorbel
Affiliation:
School of Computer Sciences, Tunisia
Keyword(s):
Artificial Neural Networks, Classifier Stability, Dimension Reduction, Error Rate Probability Density Function, Kernel-diffeomorphism Plug-in Algorithm, Patrick-Fischer Distance Estimator, Stability.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
In the industrial field, the artificial neural network classifiers are currently used and they are generally
integrated of technologic systems which need efficient classifier. Statistical classifiers also have been
developed in the same direction and different associations and optimization procedures have been proposed
as Adaboost training or CART algorithm to improve the classification performance. However, the objective
comparison studies between these novel classifiers stay marginal. In the present work, we intend to evaluate
with a new criterion the classification stability between neural networks and some statistical classifiers based
on the optimization Fischer criterion or the maximization of Patrick-Fischer distance orthogonal estimator.
The stability comparison is performed by the error rate probability densities estimation which is valorised by
the performed kernel-diffeomorphism Plug-in algorithm. The results obtained show that the statistical
approaches are more stable co
mpared to the neural networks.
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