Earticle

초록

영어

This study suggests a frequency matrix technique to predict personal credit rate more efficiently using incomplete data sets. At first this study test on multiple discriminant analysis and logistic regression analysis for predicting personal credit rate with incomplete data sets. Missing values are predicted with mean imputation method and regression imputation method here. An artificial neural network and frequency matrix technique are also tested on their performance in predicting personal credit rating. A data set of 8,234 customers in 2004 on personal credit information of Bank A are collected for the test. The performance of frequency matrix technique is compared with that of other methods. The results from the experiments show that the performance of frequency matrix technique is superior to that of all other models such as MDA-mean, Logit-mean, MDA-regression, Logit-regression, and artificial neural networks.

목차

Abstract
 1. 서론
 2. 이론적 고찰
  2.1 예측변수의 결측값 처리 알고리즘
  2.2 결측값 대체 방법론 관련 연구
  2.3 개인신용평가의 일반적 고찰
  2.4 연결빈도행렬과 브레인 매핑
 3. 연구 방법
  3.1 자료수집과 사전처리
  3.2 자료 분석
  3.3 분석절차
 4. 연구 모형
  4.1 결측치 대체한 데이터를 이용한 모형 구축
  4.2 결측치 포함한 데이터를 이용한 모형구축
  4.3 개인신용예측모형 비교분석
 5. 연구 결과
 6. 결론
 참고문헌

키워드

저자

• 배재권 [ Jae Kwon Bae | 서강대학교 경영학과 박사과정 ] 교신저자
• 김진화 [ Jinhwa Kim | 서강대학교 경영학과 부교수 ]
• 황국재 [ Kook Jae Hwang | 서강대학교 경영학과 부교수 ]

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