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A GA-based fuzzy decision tree approach for corporate bond rating

Title
A GA-based fuzzy decision tree approach for corporate bond rating
Authors
Shin K.-S.Kim H.-J.Kwon S.-B.
Ewha Authors
신경식김현정
SCOPUS Author ID
신경식scopus; 김현정scopus
Issue Date
2004
Journal Title
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
ISSN
0302-9743JCR Link
Citation
vol. 3157, pp. 505 - 514
Indexed
SCOPUS WOS scopus
Abstract
The induction based on a tree structure is an appropriate representation of the complex human reasoning process such as a corporate bond rating application. Furthermore, the fuzzy decision tree (FDT) can handle the information about vague and incomplete classification knowledge represented in human linguistic terms. In addition, FDT is more flexible by relaxing the constraint of mutual exclusivity of cases in decision tree. We propose a hybrid approach using FDT and genetic algorithms (GA) enhances the effectiveness of FDT to the problem of corporate bond rating classification. This study utilizes a hybrid approach using GA in an attempt to find an optimal or near optimal hurdle values of membership function in FDT. The results show that the accuracy of the integrated approach proposed for this study increases overall classification accuracy rate significantly. We also show that the FDT approach increases the flexibility of the classification process. © Springer-Verlag Berlin Heidelberg 2004.
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경영대학 > 경영학전공 > Journal papers
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