Earticle

초록

영어

As the great disturbance from abusing credit cards in Korea becomes stabilized, credit card companies need to interpret credit-card delinquents classification models from the viewpoint of profit. However, hit ratio which has been used as a measure of goodness of classification models just tells us how much correctly they classified rather than how much profits can be obtained as a result of using classification models. In this research, we tried to develop a new utility-based measure from the viewpoint of profit and then used this new measure to analyze two classification models(Neural Networks and Decision Tree models). We found that the hit ratio of neural model is higher than that of decision tree model, but the utility value of decision tree model is higher than that of neural model. This experiment shows the importance of utility based measure for credit-card delinquents classification models. We expect this new measure will contribute to increasing profits of credit card companies.

목차

Abstract
 1. 서론
 2. 문헌연구
  2.1 신용 분류 모형
  2.2 유틸리티 기반 데이터마이닝(Utility-Based Data Mining)
 3. 데이터 및 실험 모형
  3.1 실험 데이터
  3.2 실험 모형
 4. 실험결과 및 해석
 5. 결론
 참고문헌

키워드

저자

• 정석훈 [ Sukhoon Chung | 고려대학교 경영대학 박사과정 ]
• 서용무 [ Yongmoo Suh | 고려대학교 경영대학 교수 ]

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