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Fuzzy genetics-based machine learning (FGBML) is one of the representative approaches to obtain a set of fuzzy if-then rules by evolutionary computation. A number of FGBML methods have been proposed so far. Among them, Michigan-style approaches are popular thanks to thier lower computational cost than Pittsburgh approaches. In this study, we introduce two simple modifications for our Michigan-style...
Fuzzy genetics-based machine learning (FGBML) has frequently been used for fuzzy classifier design. It is one of the promising evolutionary machine learning (EML) techniques from the viewpoint of data mining. This is because FGBML can generate accurate classifiers with linguistically interpretable fuzzy if-then rules. Of course, a classifier with tens of thousands of if-then rules is not linguistically...
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