José M. Cadenas, M. Carmen Garrido, Raquel Martínez


Classification is an important task in Data Mining. In order to carry out classification, many classifiers require a previous preparatory step for their data. In this paper we focus on the process of discretization of attributes because this process is a very important part in Data Mining. In many situations, the values of the attributes present imprecision because imperfect information inevitably appears in real situations for a variety of reasons. Although, many efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty and imprecision that can be handled. Therefore, in this paper we propose an algorithm to construct fuzzy partitions from imprecise information and we evaluate them in a Fuzzy Random Forest ensemble which is able to work with imprecise information too. Also, we compare our proposal with results of other works.


  1. Au, W.-H., Chan, K. C., and Wong, A. (2006). A fuzzy approach to partitioning continuous attributes for classification. IEEE Tran, Knowledge and Data Engineering, 18(5):715-719.
  2. Bonissone, P. (1997). Uncertainty Management in Information Systems: From Needs to Solutions, chapter Approximate reasoning systems: handling uncertainty and imprecision in information systems, pages 369- 395. A. Motro and Ph. Smets, Eds. Kluwer Academic Publishers.
  3. Bonissone, P., Cadenas, J. M., Garrido, M., and DíazValladares, R. (2010). A fuzzy random forest. Int. J. Approx. Reasoning, 51(7):729-747.
  4. Cadenas, J., Garrido, M., Martínez, R., and Mun˜oz, E. (2010). Ofp class: An algorithm to generate optimized fuzzy partitions to classification. In 2nd International Conference on Fuzzy Computation, ICFC2010., pages 5-13.
  5. Cantu-Paz, E. and Kamath, C. (2001). Data Mining: A heuristic approach, chapter On the use of evolutionary algorithms in data mining, pages 48-71. Ideal Group Publishing.
  6. Casillas, J. and Sánchez, L. (2006). Knowledge extraction from data fuzzy for estimating consumer behavior models. In IEEE conference on Fuzzy Systems, pages 164-170.
  7. Cox, E. (2005). Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration. Morgan Kaufmann Publishers.
  8. Garrido, M., Cadenas, J., and Bonissone, P. (2010). A classification and regression technique to handle heterogeneous and imperfect information. Soft Computing, 14(11):1165-1185.
  9. Liu, H., Hussain, F., Tan, C., and Dash, M. (2002). Discretization: an enabling technique. Journal of Data Mining and Knowledge Discovery, 6(4):393-423.
  10. Otero, A. J., Sánchez, L., and Villar, J. R. (2006). Longest path estimation from inherently fuzzy data acquired with gps using genetic algorithms. In Int. Symposium on Evolving Fuzzy Systems, pages 300-305.
  11. Palacios, A. M., Sánchez, L., and Couso, I. (2009). Extending a simple genetic coopertative-competitive learning fuzzy classifier to low quality datasets. Evolutionary Intelligence, 2:73-84.
  12. Palacios, A. M., Sánchez, L., and Couso, I. (2010). Diagnosis of dyslexia with low quality data with genetic fuzzy systems. Int. J. Approx. Reasoning, 51:993- 1009.
  13. Wang, X. and Kerre, E. (2001). Reasonable propierties for the ordering of fuzzy quantities (i-ii). Journal of Fuzzy Sets and Systems, 118:375-405.

Paper Citation

in Harvard Style

M. Cadenas J., Carmen Garrido M. and Martínez R. (2011). CONSTRUCTING FUZZY PARTITIONS FROM IMPRECISE DATA . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 379-388. DOI: 10.5220/0003644303790388

in Bibtex Style

author={José M. Cadenas and M. Carmen Garrido and Raquel Martínez},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)
SN - 978-989-8425-83-6
AU - M. Cadenas J.
AU - Carmen Garrido M.
AU - Martínez R.
PY - 2011
SP - 379
EP - 388
DO - 10.5220/0003644303790388