Polish (Poland)English (United Kingdom)

Genetic fuzzy classifier with fuzzy rough sets for imprecise data

Type of Publication: In Proceedings Keywords: approximation theory;fuzzy set theory;genetic algorithms;pattern classification;rough set theory;fuzzy antecedent sets;fuzzy method;fuzzy rough set theory;genetic fuzzy classifier;input data imprecision handling;nonsingleton fuzzy premise sets;rough
Year: 2014
Authors:
  • J. T. Starczewski
  • R. K. Nowicki
  • B. A. Nowak
Book title: Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
Pages: 1382-1389
Month: July
BibTex:
Abstract:
The main problem addressed in this paper is to handle adequately imprecision of input data by means of a combination of fuzzy methods with the rough set theory. We will make use of fuzzy rough sets derived as rough approximations of fuzzy antecedent sets by non-singleton fuzzy premise sets in a fuzzy classifier. Adaptation of the parameters of this system will be done by the standard genetic algorithm.

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