Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Finding fuzzy classification rules using data mining techniques
Pattern Recognition Letters
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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Fuzzy classification rules are widely used for classification, as they are more interpretable as well as efficient in handling the real-world problems, which involves imprecision and vagueness. Genetic algorithms are proven stochastic search techniques employed in automatic generation of fuzzy classification rule. However, genetic algorithms employed for the said task require large number of fitness evaluation or performance evaluations in achieving a reasonable solution requiring a large amount of computational time. Hence, to expedite the execution is a major concern in genetic algorithms. In this paper, we incorporate fitness inheritance mechanism in genetic algorithms to design a scalable genetic fuzzy classifier, which reduce the number of actual fitness function evaluations of subsequent generations and produce rules with acceptable classification accuracy.