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

Kyung Shik Shin, Hyun Jung Kim, Suhn Beom Kwon

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)505-514
Number of pages10
JournalLecture Notes in Computer Science
StatePublished - 2004
Event8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence - Auckland, New Zealand
Duration: 9 Aug 200413 Aug 2004


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