Paper Info
Title
A comparative study on heuristic algorithms for generating fuzzydecision trees
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 twofold. 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
Year
DOI
Venue
2001
10.1109/3477.915344
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Keywords
DocType
Volume
fuzzy decision tree,comparative study,analytic comparison,expanded attribute selection,optimal fuzzy decision tree,appropriate heuristic,comparative strength,experimental comparison,heuristic algorithm,fuzzy representation,fuzzy decision tree induction,fuzzydecision tree
Journal
31
Issue
ISSN
Citations 
2
1083-4419
24
PageRank 
References 
Authors
1.05
14
3
Name
Order
Citations
PageRank
X. -Z. Wang1554.97
D. S. Yeung285338.99
E. C. C. Tsang371431.47