Earticle

초록

영어

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

목차

Abstract
 Ⅰ. Introduction
 Ⅱ. Literature Review
  2.1 Feature selection
  2.2 Multi-Class Support Vector Machines
 Ⅲ. Research Framework
 Ⅳ. Experiments and Results
  4.1 Data
  4.2 Feature selection
  4.3 SVMs Model Specification and Results
 Ⅴ. Conclusions
 References

키워드

저자

• Taeho Hong [ Associate Professor, School of Business, Pusan National University ]
• Jiyoung Park [ Post-Doc, BK21 Research and Education Institute, Pusan National University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658