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.

References

  1. Breiman, L., 1996. Bagging predictors, Machine learning, vol. 24, no. 2, pp. 123-140.
  2. Drira, W., Ghorbel, F., 2012. An estimator of the L-2- probabilistic dependence measure for vectorial reduction dimension. Multiclass case, Traitement du Signal, 29(1-2), 143-155.
  3. Fukunaga, K., 1990. Introduction to Statistical Pattern Recognition, Academic Press, 2nd edition.
  4. Ghorbel, F., LT, J., 1990. Automatic control of lamellibranch larva growth using contour invariant feature extraction. Pattern Recognition.
  5. Othman, I.B., Ghorbel, F., 2013. The Use of the Modified Semi-bounded Plug-in Algorithm to Compare Neural and Bayesian Classifiers Stability, Neural Networks and Fuzzy Systems.
  6. Paliwal, M., Kumar, U. A., 2009. Neural networks and statistical techniques: A review of applications, Expert Syst. Appl., vol. 36, no. 1, pp. 2-17.
  7. Patuwo, W., Hu, M.Y., Hung, M.S., 1993. Two-group classification using neural networks, Decision Sciences, vol. 24, pp. 825-845.
  8. Saoudi, S., Ghorbel, F., Hillion, A., 1994. Nonparametric probability density function estimation on a bounded support: applications to shape classification and speech coding, Applied Stochastic Models and Data Analysis Journal, vol. 10, no. 3, pp. 215-231.
  9. Saoudi, S., Troudi, M., Ghorbel, F., 2009. An iterative soft bit error rate estimation of any digital communication systems using a non parametric probability density function, Eurasip Journal on wirless Communications and Networking.
  10. Steven, K., Rogers, Kabrisky, M., 1991. An Introduction to Biological and Artificial Neural Networks for Pattern Recognition, SPIE Optical Engineering Press, vol. 4.
  11. Tam, K.Y., Kiang, M.Y., 1992. Managerial applications of neural networks: The case of bank failure predictions, Management Science, vol. 38, no. 7, pp. 926-947.
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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