Title :
Learning Fuzzy Rule Based Classifier with Rule Weights Optimization and Structure Selection by a Genetic Algorithm
Author :
Evsukoff, Alexandre G.
Author_Institution :
Univ. Fed. do Rio de Janeiro, Rio de Janeiro
Abstract :
This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimized in a structure selection search, performed by a genetic algorithm The method is tested against benchmark classification problems found in the literature, with good results.
Keywords :
computational complexity; computational linguistics; fuzzy set theory; genetic algorithms; learning (artificial intelligence); least squares approximations; pattern recognition; benchmark classification problems; genetic algorithm; learning fuzzy rule; least squares sense; linguistic rules; model complexity; pattern recognition; rule weights optimization; structure selection; structure selection search; Association rules; Benchmark testing; Data mining; Design optimization; Fuzzy sets; Fuzzy systems; Genetic algorithms; Input variables; Machine learning; Parameter estimation;
Conference_Titel :
Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International
Conference_Location :
London
Print_ISBN :
1-4244-1209-9
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2007.4295471
