NEW RESULTS ON DIAGNOSIS BY FUZZY PATTERN RECOGNITION

Mohamed Saïd Bouguelid, Moamar Sayed Mouchaweh, Patrice Billaudel

2007

Abstract

We use the classification method Fuzzy Pattern Matching (FPM) to realize the industrial and medical diagnosis. FPM is marginal, i.e., its global decision is based on the selection of one of the intermediate decisions. Each intermediate decision is based on one attribute. Thus, FPM does not take into account the correlation between attributes. Additionally, FPM considers the shape of classes as convex one. Finally the classes are considered as equi-important by FPM. These drawbacks make FPM unusable for many real world applications. In this paper, we propose improving FPM to solve these drawbacks. Several synthetic and real data sets are used to show the performances of the Improved FPM (IFPM) with respect to classical one as well as to the well known classification method K Nearest Neighbours (KNN). KNN is known to be preferment in the case of data represented by correlated attributes or by classes with different a priori probabilities and non convex shape.

References

  1. Cadenas, J.M., M.C. Garrido and J.J. Hernandez, 2004. Improving fuzzy pattern matching techniques to deal with non discrimination ability features. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 5708-5713.
  2. Denoeux, T., M. Masson and B. Dubuisson, 1998. Advanced pattern recognition techniques for system monitoring and diagnosis: a survey. In: Journal Européen des Systèmes Automatisés (RAIRO-APIIJESA), Vol. 31(9-10), pp. 1509-1539.
  3. Denoeux, T. and L.M. Zouhal, 2001. Handling possibilistic labels in pattern classification using evidential reasoning. In: Fuzzy Sets and Systems, Vol. 1(22), pp. 409-424.
  4. Devillez, A., 2004a. Four supervised classification methods for discriminating classes of non convex shape. In: Fuzzy sets and systems, Vol. 141, pp. 219- 240.
  5. Devillez, A., M.Sayed Mouchaweh and P. Billaudel, 2004b. A process monitoring module based on fuzzy logic and Pattern Recognition. In: International Journal of Approximate Reasoning, Vol. 37, Issue 1, pp.43-70.
  6. Dubois, D. and H. Prade, 1993. On possibility/probability transformations, In: Fuzzy Logic, pp. 103-112.
  7. Duda, R.O., P.E. Hart and D.E. Stork, 2001. Pattern Classification second edition. Wiley, New York
  8. Dubuisson, B., 2001. Automatique et statistiques pour le diagnostic. Traité IC2 Information, commande, communication. Hermes Sciences, Paris.
  9. Dubuisson, B., 1990. Diagnostic et reconnaissance des formes, Traité des Nouvelles Technologies, série Diagnostic et Maintenance, Hermes Sciences, Paris.
  10. Grabish, M. and M. Sugeno, 1992. Multi-attribute classification using fuzzy integral. In: Proc. of fuzzy IEEE, pp. 47-54.
  11. Medasani, S., K. Jaeseok and R. Krishnapuram, 1998, An overview of membership function generation techniques for pattern recognition. In: International Journal of Approximate Reasoning,Vol. 19,pp. 391- 417.
  12. Newman D.J., Hettich, S., Blake C.L. and Merz, C.J, 1998. UCI Repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository.html, Dept. of Information and Computer Science, University of California, Irvine.
  13. Ripley, B.D.,1996. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge.
  14. Sayed Mouchaweh, M., A. Devillez, G.V. Lecolier and P. Billaudel, 2002a. Incremental learning in real time using fuzzy pattern matching. In: Fuzzy Sets and Systems, Vol. 132/1, pp. 49-62.
  15. Sayed Mouchaweh, M. and P. Billaudel ,2002b. Influence of the choice of histogram parameters at Fuzzy Pattern Matching performance. In: WSEAS Transactions on Systems, Vol. 1, Issue 2, pp. 260-266
  16. Sayed Mouchaweh, M., 2004. Diagnosis in real time for evolutionary processes in using Pattern Recognition and Possibility theory. In: International Journal of Computational Cognition 2, Vol. 1, pp. 79-112.
  17. Zadeh, L. A., 1978. Fuzzy sets as a basis for a theory of possibility. In: Fuzzy sets and systems, Vol. 1, pp. 3-2.
Download


Paper Citation


in Harvard Style

Saïd Bouguelid M., Sayed Mouchaweh M. and Billaudel P. (2007). NEW RESULTS ON DIAGNOSIS BY FUZZY PATTERN RECOGNITION . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-82-5, pages 167-172. DOI: 10.5220/0001628401670172


in Bibtex Style

@conference{icinco07,
author={Mohamed Saïd Bouguelid and Moamar Sayed Mouchaweh and Patrice Billaudel},
title={NEW RESULTS ON DIAGNOSIS BY FUZZY PATTERN RECOGNITION},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2007},
pages={167-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001628401670172},
isbn={978-972-8865-82-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - NEW RESULTS ON DIAGNOSIS BY FUZZY PATTERN RECOGNITION
SN - 978-972-8865-82-5
AU - Saïd Bouguelid M.
AU - Sayed Mouchaweh M.
AU - Billaudel P.
PY - 2007
SP - 167
EP - 172
DO - 10.5220/0001628401670172