Structure Identification of Fuzzy Classifiers
نویسندگان
چکیده
Data-based identification of fuzzy rule-based classifiers for high-dimensional problems is addressed. A crisp binary decision tree approach is proposed for the selection of the relevant features and effective initial partitioning of the input domain. The decision tree is then transformed into a fuzzy rule-based classifier. Fuzzy classifiers have more flexible decision boundaries than decision trees and can therefore be more parsimonious. Therefore, the DT initialized fuzzy classifier is reduced in an iterative scheme by means of similarity-based rulereduction. A genetic algorithm with a multi-objective criterion searching for both redundancy and accuracy is applied. The proposed approach is studied on an artificial problem and the Wisconsin Breast Cancer classification problem.
منابع مشابه
Structure Identification of Fuzzy Classifers
For complex and high-dimensional problems, data-driven identification of classifiers has to deal with structural issues like the selection of the relevant features and effective initial partition of the input domain. Therefore, the identification of fuzzy classifiers is a challenging topic. Decision-tree (DT) generation algorithms are effective in feature selection and extraction of crisp class...
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