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A variety of fuzzy genetics-based machine learning algorithms have been proposed in the frameworks of Michigan and Pittsburgh approaches. Since each individual is a single rule, Michigan-style algorithms need much less computation time than Pittsburgh-style algorithms where each individual is a rule set. For the same reason, Michigan-style algorithms cannot directly optimize rule sets. Rule set optimization...
In the design of rule-based classifiers, a single rule is often generated from a single pattern in a heuristic manner. Since the generated rule is likely to be over-specialized to the pattern, its conditions are often randomly replaced with don't care. However, the generalized rule with don't care conditions does not always have high classification ability. This is because the replacement is randomly...
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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