Earticle

초록

영어

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first, we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

목차

Abstract
 1. 서론
 2. 문헌 연구
  2.1 통계적 방법을 이용한 분류
  2.2 인공지능(Artificial Intelligence) 방법을 이용한 분류
 3. 실험의 준비
  3.1 자료의 선정
  3.2 변수의 선정
 4. 실험 방법
  4.1 다변량 판별분석
  4.2 일반적인 신경회로망 모형
  4.3 신경회로망의 분리 모형
  4.4 사례 기반 추론을 이용한 실험
 5. 모형별 실험 결과 비교
 6. 결론
 참고문헌

키워드

저자

• 김다윗 [ David Kim | 한국신용정보 ]
• 민성환 [ Sung-Hwan Min | 한림대학교 경영학과 조교수 ] 교신저자
• 한인구 [ Ingoo Han | 한국과학기술원 테크노경영대학원 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005