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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
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)
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)
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)
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)
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)
Casillas, J., Sánchez, L.: Knowledge extraction from data fuzzy for estimating consumer behavior models. In: IEEE Confer. on Fuzzy Systems, pp. 164–170 (2006)
Cox, E.: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration. Morgan Kaufmann Publishers (2005)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cadenas, J.M., Garrido, M.C., Martínez, R. (2013). Generating Fuzzy Partitions from Nominal and Numerical Attributes with Imprecise Values. In: Madani, K., Dourado, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2011. Studies in Computational Intelligence, vol 465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35638-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-35638-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35637-7
Online ISBN: 978-3-642-35638-4
eBook Packages: EngineeringEngineering (R0)