A comparative study on heuristic algorithms for generating fuzzy decision trees

Author

Wang, X.Z. ; Yeung, D.S. ; Tsang, E.C.C.

Author_Institution

Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China

Volume

31

Issue

2

fYear

2001

fDate

4/1/2001 12:00:00 AM

Firstpage

215

Lastpage

226

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

Keywords

decision trees; fuzzy set theory; heuristic programming; learning by example; attribute selection; earning from examples; fuzzy decision trees; fuzzy representation; heuristic algorithms; Algorithm design and analysis; Buildings; Decision trees; Expert systems; Fuzzy reasoning; Guidelines; Heuristic algorithms; Induction generators; Knowledge based systems; Uncertainty;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.915344

Filename

915344