Generating Fuzzy Partitions from Nominal and Numerical Attributes with Imprecise Values

Part of the Studies in Computational Intelligence book series (SCI, volume 465)

Abstract

In areas of Data Mining and Soft Computing is important the discretization of numerical attributes because there are techniques that can not work with numerical domains or can get better results when working with discrete domains. The precision obtained with these techniques depends largely on the quality of the discretization performed. Moreover, in many real-world applications, data from which the discretization is carried out, are imprecise. In this paper we address both problems by proposing an algorithm to obtain a fuzzy discretization of numerical attributes from input data that show imprecise values in both numerical and nominal attributes. To evaluate the proposed algorithm we analyze the results on a set of datasets from different real-world problems.

Keywords

Fuzzy partition Imperfect information Fuzzy random forest ensemble Imprecise data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of InformaticUniversity of MurciaMurciaSpain

Personalised recommendations