A framework for evolving fuzzy rule

This work presents a framework for genetic fuzzy rule based classifier. First, a classification problem is divided into several two-class problems following a fuzzy class binarization scheme; next, a fuzzy rule is evolved for each two-class problem using a Michigan iterative learning approach; finally, the evolved fuzzy rules are integrated using the fuzzy class binarization scheme. In particular, some encoding schemes are implemented following the proposed framework and their performance is compared. Experiments are conducted with different public available data sets.

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