원문정보
초록
영어
Corporate credit-rating prediction using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multiclass 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 multiclass classification, since SVMs were originally devised for binary classification. However, the studies that applied MSVMs in predicting
bond rating just adopted a few techniques. This study is designed to evaluate all MSVM techniques proposed in the literature for the prediction of corporate bond rating in Korea. To do this, we applied six different techniques of MSVMs, and compared the prediction performances. We also examined some modified version of conventional MSVM techniques.
목차
Introduction
Six Techniques for Implementing MulticlassSupport Vector Machines
Constructing Several Binary Classifiers:(1) One-Against-All
Constructing Several Binary Classifiers:(2) One-Against-One
Constructing Several Binary Classifiers:(3) DAGSVM
Constructing Several Binary Classifiers:(4) ECOC
Directly Considering All the Data at Once:(5) Method of Weston and Watkins
Directly Considering All the Data at Once:(6) Method of Crammer and Singer
Prior Studies on Bond Rating using MSVMs
Experimental Design and Results
Research data
Experimental design
Experimental Results
Concluding Remarks
References