A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach

Predicting corporate credit-rating using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multi-class support vector machines (MSVMs) have become a very appealing machine-learning approach due to their good perfo...

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Bibliographic Details
Published in:Computers & operations research 2012-08, Vol.39 (8), p.1800-1811
Main Authors: Kim, Kyoung-jae, Ahn, Hyunchul
Format: Article
Language:English
Subjects:
Artificial intelligence
Bond ratings
Categories
Classification
Comparative analysis
Corporate credit rating
Credit ratings
Mathematical models
Multi-class classification
Operations research
Ordinal pairwise partitioning
Partitioning
Ratings
Samples
Statistical methods
Support vector machines
Validation studies
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Summary:Predicting corporate credit-rating using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multi-class support vector machines (MSVMs) have become a very appealing machine-learning approach due to their good performance. Until now, researchers have proposed a variety of techniques for adapting support vector machines (SVMs) to multi-class classification, since SVMs were originally devised for binary classification. However, most of them have only focused on classifying samples into nominal categories; thus, the unique characteristic of credit-rating – ordinality – seldom has been considered in the proposed approaches. This study proposes a new type of MSVM classifier (named OMSVM) that is designed to extend the binary SVMs by applying an ordinal pairwise partitioning (OPP) strategy. Our model can efficiently and effectively handle multiple ordinal classes. To validate OMSVM, we applied it to a real-world case of bond rating. We compared the results of our model with those of conventional MSVM approaches and other AI techniques including MDA, MLOGIT, CBR, and ANNs. The results showed that our proposed model improves the performance of classification in comparison to other typical multi-class classification techniques and uses fewer computational resources.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2011.06.023