Title :
Genetic fuzzy classifier with fuzzy rough sets for imprecise data
Author :
Starczewski, Janusz T. ; Nowicki, Robert K. ; Nowak, Bartosz A.
Author_Institution :
Inst. of Comput. Intell., Czestochowa Univ. of Technol., Czestochowa, Poland
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.
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 approximations; standard genetic algorithm; Approximation methods; Equations; Fuzzy logic; Fuzzy sets; Fuzzy systems; Optimization; Rough sets;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-2073-0
DOI :
10.1109/FUZZ-IEEE.2014.6891857
