Earticle

초록

영어

Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

목차

ABSTRACT
 Ⅰ. Introduction
 Ⅱ. Literature Review
  2.1. Support Vector Machine (SVM)
  2.2. SVM+
  2.3. SVM+MTL (Multi-Task Learning)
  2.4. Corporate Credit Rating
  2.5. Multi-Classification Methods
 Ⅲ. Research Framework
 Ⅳ. Experiments and Analysis
  4.1. Datasets
  4.2. Experimental Design
  4.3. Results Analysis
 Ⅴ. Conclusion
 

키워드

저자

• Gang Ren [ Doctoral Candidate, Department of Business Administration, Pusan National University, Korea ]
• Taeho Hong [ Professor, Department of Business Administration, Pusan National University, Korea ] Corresponding Author
• YoungKi Park [ Assistant Professor, Department of Information Systems and Technology Management, George Washington University, USA ]

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