Learning Fuzzy Classifiers with Evolutionary Algorithms
Mauro L. Beretta, Andrea G. B. Tettamanzi · 2003
This paper illustrates an evolutionary algorithm, which learns classifiers, represented as sets of fuzzy rules, from a data set containing past experimental observations of a phenomenon. The approach is applied to a benchmark dataset made available by the machine learning community. 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.