OFP_CLASS: AN ALGORITHM TO GENERATE OPTIMIZED FUZZY PARTITIONS TO CLASSIFICATION

José M. Cadenas, M. del Carmen Garrido, Raquel Martínez, Enrique Muñoz

Abstract

The discretization of values is a important role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means a series of fuzzy sets which constitute a fuzzy partition of this attribute’s domain. We present an algorithm, which carries out a fuzzy discretization of continuous attributes in two stages. In the first stage a fuzzy decision tree is used and the genetic algorithm is used in the second stage. In this second stage the cardinality of the partition is defined. After defining the fuzzy partitions these are evaluated by a fuzzy decision tree which is also detailed in this study.

References

  1. Asuncion, A. and Newman, D. (2007). Uci machine learning repository. http://www.ics.uci.edu/ mlearn/MLRepository.html.
  2. Au, W.-H., Chan, K. C. C., and Wong, A. K. C. (2006). A fuzzy approach to partitioning continuous attributes for classification. IEEE Trans. on Knowl. and Data Eng., 18(5):715-719.
  3. Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell, MA, USA.
  4. Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. In EWSL-91: Proceedings of the European working session on learning on Machine learning, pages 164-178, New York, NY, USA. Springer-Verlag New York, Inc.
  5. Cox, E. (2005). Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration. Morgan Kaufmann Publishers.
  6. Cox, E., Taber, R., and OHagan, M. (1998). The Fuzzy Systems Handbook. AP Professional, 2 edition.
  7. Garca, S., Fernndez, A., Luengo, J., and Herrera, F. (2009). A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput., 13(10):959- 977.
  8. Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. In Machine Learning, pages 63-91.
  9. Kbir, M. A., Maalmi, K., Benslimane, R., and Benkirane, H. (2000). Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules. Pattern Recogn. Lett., 21(6-7):503-509.
  10. Li, C. (2009). A combination scheme for fuzzy partitions based on fuzzy majority voting rule. In NSWCTC 7809: Proceedings of the 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing, pages 675-678, Washington, DC, USA. IEEE Computer Society.
  11. Li, C., Wang, Y., and Dai, H. (2009). A combination scheme for fuzzy partitions based on fuzzy weighted majority voting rule. Digital Image Processing, International Conference on, 0:3-7.
  12. Liu, H., Hussain, F., Tan, C. L., and Dash, M. (2002). Discretization: An enabling technique. Data Min. Knowl. Discov., 6(4):393-423.
  13. Piero, P., Arco, L., Garca, M., and Acevedo, L. (2003). Algoritmos genticos en la construccion de funciones de pertenencia borrosas. Revista Iberoamericana de Inteligencia Artificial, 18:25-35.
  14. Quilan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
Download


Paper Citation


in Harvard Style

M. Cadenas J., del Carmen Garrido M., Martínez R. and Muñoz E. (2010). OFP_CLASS: AN ALGORITHM TO GENERATE OPTIMIZED FUZZY PARTITIONS TO CLASSIFICATION . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICFC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 5-13. DOI: 10.5220/0003052700050013


in Bibtex Style

@conference{icfc10,
author={José M. Cadenas and M. del Carmen Garrido and Raquel Martínez and Enrique Muñoz},
title={OFP_CLASS: AN ALGORITHM TO GENERATE OPTIMIZED FUZZY PARTITIONS TO CLASSIFICATION},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICFC, (IJCCI 2010)},
year={2010},
pages={5-13},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003052700050013},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICFC, (IJCCI 2010)
TI - OFP_CLASS: AN ALGORITHM TO GENERATE OPTIMIZED FUZZY PARTITIONS TO CLASSIFICATION
SN - 978-989-8425-32-4
AU - M. Cadenas J.
AU - del Carmen Garrido M.
AU - Martínez R.
AU - Muñoz E.
PY - 2010
SP - 5
EP - 13
DO - 10.5220/0003052700050013