Comparison of Statistical and Artificial Neural Networks Classifiers by Adjusted Non Parametric Probability Density Function Estimate

Ibtissem Ben Othman, Wissal Drira, Faycel El Ayeb, Faouzi Ghorbel

2015

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 compared to the neural networks.

Paper Citation

in Harvard Style

Othman I., Drira W., El Ayeb F. and Ghorbel F. (2015). Comparison of Statistical and Artificial Neural Networks Classifiers by Adjusted Non Parametric Probability Density Function Estimate . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 672-678. DOI: 10.5220/0005360906720678

in Bibtex Style

@conference{visapp15,
author={Ibtissem Ben Othman and Wissal Drira and Faycel El Ayeb and Faouzi Ghorbel},
title={Comparison of Statistical and Artificial Neural Networks Classifiers by Adjusted Non Parametric Probability Density Function Estimate},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={672-678},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005360906720678},
isbn={978-989-758-089-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Comparison of Statistical and Artificial Neural Networks Classifiers by Adjusted Non Parametric Probability Density Function Estimate
SN - 978-989-758-089-5
AU - Othman I.
AU - Drira W.
AU - El Ayeb F.
AU - Ghorbel F.
PY - 2015
SP - 672
EP - 678
DO - 10.5220/0005360906720678