Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume 31, Issue 2, Pages 215-226Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/3477.915344
Keywords
approximate reasoning; fuzzy decision trees; fuzzy rules; heuristic algorithms; learning; learning from fuzzy examples
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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. Ln 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.
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