Earticle

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

Abstract
 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

키워드

저자

• Hyunchul Ahn [ Department of Business Administration, College of Social Sciences, Sungshin Women’s University ]
• Kyoung-jae Kim [ Department of Management Information Systems, Dongguk University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658