Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier

Cristhian Molina, Vincent Bombardier, Patrick Charpentier

2015

Abstract

In this paper, two ways for automatically designing a hierarchical classifier is checked. This study deals with a specific context where is necessary to work with a few number of training samples (and often unbalanced), to manage the subjectivity of the different output classes and to take into account an imprecision degree in the input data. The aim is also to create an interpretable classification system by reducing its dimensionality with the use of Feature Selection and Fuzzy Association Rules generation. The obtained results over an industrial wood datasets prove their efficacy to select input feature and they are used to make some conclusions about their performance. Finally, an original methodology to automatically build a hierarchical classifier is proposed by merging the both previous methods. Each node of the hierarchical structure corresponds to a Fuzzy Rules Classifier with selected inputs and macro classes for output. The leaves are the outputs of the classification system.

References

  1. Alcala-Fernandez, J., Alcala, R., and Herrera, F. (2011). A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning, IEEE Transactions on Fuzzy Systems, 19 (5), 857-872.
  2. Bombardier, V., and Schmitt, E. Measure (2010). Fuzzy rule classifier: Capability for generalization in wood color recognition, Engineering Applications of Artificial Intelligence, 23 (6), 978-988.
  3. Bombardier V., Mazaud C., Lhoste P. Vogrig R. (2007) Contribution of Fuzzy Reasoning Method to knowledge Integration in a wood defect Recognition System. Computers in Industry Journal 58:355-366
  4. Chen, Y. C., Pal, N. R., and Chung, I.F. (2012). An Integrated Mechanism for Feature Selection and Fuzzy Rule Extraction for Classification, IEEE Transactions on Fuzzy Systems, 20 (4), 683-698.
  5. De Lannoy, G., François, D., and Verleysen, M. (2011). Class-Specific Feature Selection for One-Against-All Multiclass SVMs, European Symposium on Artificial Neu. Net., Computacional Intel. and Mach. Learn.
  6. Ferreira, A. J, and Figueiredo, M. A. (2012). Efficient feature selection filters for high-dimensional data, Pattern Recognition
  7. Gordon, A. D. (1987). A review of hierarchical Classification. Journal of the Royal Society. Series A, 150 (2), 119-137.
  8. Grandvalet, Y., and Canu, S. (2003). Adaptive scaling for feature selection in SVMs, in Neural Information Processing System. Cambridge, MA: MIT Press.
  9. Guyon, I., and Elisseeff, A. (2003). An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157-1182.
  10. Han, J., Kamber, M., and Pei, J. (2006). Data Mining: Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann.
  11. Horng, S.C., and Hsiao, Y.L. (2009). Fuzzy clustering decision tree for classifying working wafers of ion implanter, IEEE International Conference on Industrial Engineering and Engineering Management, 703-707.
  12. Hühn, J., and Hüllermeier, E. (2009). FURIA: an algorithm for unordered fuzzy rule induction, Data Mining and Knowledge Discovery, 19 (3), 293-319.
  13. Ishibuchi, H., Nozaki, K., Tanaka, H., (1992). Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems 52, 21- 32.
  14. Kira, K., and Rendell, L.A. (1992). The feature selection problem: Traditional methods and a new algorithm, Proceedings of Ninth National Conference on Artificial Intelligence, 129-134.
  15. Langley, P. (1994). Selection of relevant features in machine learning, Proceedings of the AAAI Fall Symposium on Relevance, 1-5.
  16. Li, G.Z., Yang, J., Liu, G.P., and Xue, L. (2004). Feature selection for multi-class problems using support vector machines, Lect. Notes in comp. science, 3157, 292- 300.
  17. Liu, Wang, L., Zhang, J., Yin, J., and Liu, H. (2014). Global and Local structure Preservation for Feature Selection, IEEE trans. Neu. net. and learn. Sys., 25 (6).
  18. Nakashima, T., Schaefer, G., Yokota, Y., and Ishibuchi, H. (2007). A weighted fuzzy classifier and its application to image processing tasks, Fuzzy Sets and Systems, 158, 284-294.
  19. Pudil, P., Novovicova, J., and Kittler, J. (1994).Floating search methods in feature selection, Pattern recognition letters 15, 1119-1125.
  20. Schmitt, E., Bombardier, V., and Wendling, L. (2008). Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral, IEEE trans. On Systems, mand and Cybernetics-Part B: Cybernetics, 38 (5), October.
  21. Wang, F., Man, L., Wang, B., Xiao, Y., Pan, W., Lu, X. (2008) Fuzzy-based algorithm for color recognition of license plates, Pattern Recognition Letters 29, 1007- 1020.
  22. Zhang, C., and Zhang, S. (2002). Association Rule Mining: Models and Algorithms. Berlin, Heidelberg: Springer-Verlag.
  23. Zhao, Z., Wang, L., Liu, H., and Ye, J. (2013). On Similarity preserving Feature Selection, IEEE Trans. Knowledge and Data engineering, 25 (3).
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Paper Citation


in Harvard Style

Molina C., Bombardier V. and Charpentier P. (2015). Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 150-155. DOI: 10.5220/0005595401500155


in Bibtex Style

@conference{fcta15,
author={Cristhian Molina and Vincent Bombardier and Patrick Charpentier},
title={Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (ECTA 2015)},
year={2015},
pages={150-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005595401500155},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (ECTA 2015)
TI - Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier
SN - 978-989-758-157-1
AU - Molina C.
AU - Bombardier V.
AU - Charpentier P.
PY - 2015
SP - 150
EP - 155
DO - 10.5220/0005595401500155