Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9315
Title: A comparative study on heuristic algorithms for generating fuzzy decision trees
Authors: Wang, XZ
Yeung, DS
Tsang, ECC
Keywords: Approximate reasoning
Fuzzy decision trees
Fuzzy rules
Heuristic algorithms
Learning
Learning from fuzzy examples
Issue Date: 2001
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2001, v. 31, no. 2, p. 215-226 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: Fuzzy decision tree induction is an important way of learning from examples with fuzzy representation. Since the construction of optimal fuzzy decision tree is NP-hard, the research on heuristic algorithms is necessary. In this paper, three heuristic algorithms for generating fuzzy decision trees are analyzed and compared. One of them is proposed by the authors. The comparisons are two fold. One is the analytic comparison based on expanded attribute selection and reasoning mechanism; the other is the experimental comparison based on the size of generated trees and learning accuracy. The purpose of this study is to explore comparative strengths and weaknesses of the three heuristics and to show some useful guidelines on how to choose an appropriate heuristic for a particular problem.
URI: http://hdl.handle.net/10397/9315
ISSN: 1083-4419
DOI: 10.1109/3477.915344
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