C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Decision Tree Construction for Data Mining on Grid Computing
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
Using divide-and-conquer GA strategy in fuzzy data mining
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Intelligent intrusion detection system using fuzzy rough set based C4.5 algorithm
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Decision tree induction is one of common approaches for extracting knowledge from a sets of feature-based examples. In real world, many data occurred in a fuzzy and uncertain form. The decision tree must able to deal with such fuzzy data. This paper presents a tree construction procedure to build a fuzzy decision tree from a collection of fuzzy data by integrating fuzzy set theory and entropy. It proposes a fuzzy decision tree induction method for fuzzy data of which numeric attributes can be represented by fuzzy number, interval value as well as crisp value, of which nominal attributes are represented by crisp nominal value, and of which class has confidence factor. It also presents an experiment result to show the applicability of the proposed method.