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Michigan-style genetic algorithms are usually used for learning fuzzy classification rules from numerical examples. In these approaches, each rule is encoded as a chromosome, and then builds up the classification rule set by these chromosomes. So the fitness value can only assign to a single rule rather than a whole rule set. This makes some chromosomes characterized by the minority of instances may...
Accuracy and interpretability are two important objectives in the design of fuzzy classification system. In many real-world applications, expert experiences usually have good interpretability, but their accuracy is not always high. Applying expert experiences to fuzzy classification system can obtain better accuracy and preserve interpretability. In this paper, we present a method to translate expert...
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