Soft Computing

, Volume 15, Issue 10, pp 1945–1957 | Cite as

Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels

  • Luciano Sánchez
  • Inés Couso


Fuzzy memberships can be understood as coverage functions of random sets. This interpretation makes sense in the context of fuzzy rule learning: a random-sets-based semantic of the linguistic labels is compatible with the use of fuzzy statistics for obtaining knowledge bases from data. In particular, in this paper we formulate the learning of a fuzzy-rule-based classifier as a problem of statistical inference. We propose to learn rules by maximizing the likelihood of the classifier. Furthermore, we have extended this methodology to interval-censored data, and propose to use upper and lower bounds of the likelihood to evolve rule bases. Combining descent algorithms and a co-evolutionary scheme, we are able to obtain rule-based classifiers from imprecise data sets, and can also identify the conflictive instances in the training set: those that contribute the most to the indetermination of the likelihood of the model.


Fuzzy Rule Pareto Front Linguistic Label Precedence Operator Fuzzy Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Spanish Ministry of Science and Innovation, under grants TIN2008-06681-C06-04 and TIN2007-67418-C03-03.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of OviedoGijónSpain
  2. 2.Statistics Department, Facultad de CienciasUniversity of OviedoOviedoSpain

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