Earticle

초록

영어

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

목차

Abstract
 1. 서 론
  1.1 논문의 배경
  1.2 연구 목적과 논문 구성
 2. 관련 연구
 3. 불일치 패턴 모델 알고리즘
 4. 실험의 설계
  4.1 실험의 기본 설계
  4.2 실험에 사용된 데이터
 5. 실험결과
  5.1 데이터 특성에 따른 불일치 패턴 모델 성능 향상 실험 결과
  5.2 불일치 패턴 모델 내부사용 기법에 따른 위치도 분석 결과
  5.3 목표 변수 불균형 해소 후 불일치 패턴모델의 변화 실험 결과
 6. 결론
 참고문헌

키워드

저자

• 허준 [ Joon Hur | SPSS Korea (주)데이타솔루션 ] 주저자
• 김종우 [ Jong Woo Kim | 한양대학교 경영대학 경영학부 교수 ] 교신저자

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