산업재해 예측 모형을 위한 데이터 마이닝 기법 비교

• 영문명: A Comparison of Data Mining Techniques for Predicting Model of Industrial Accidents
• 발행기관: 한국산업경영시스템학회
• 저자명: 임영문(Leem young Moon) 유창현(Ryu Chang Hyun)
• 간행물 정보: 『한국산업경영시스템학회 학술대회』2006년 춘계학술대회 논문집, 107~113쪽, 전체 7쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2006.05.01

영문 초록

  The data mining technique is an effective instrument for making large datasets accessible and different industrial accident data comparable. Many research studies have been focused on the analysis of industrial accidents in order to reduce them. However most researches used a typical technique for the analysis of data related to industrial accidents. The main objective of this study is to compare algorithms comparison for data analysis of industrial accidents and this paper provides a comparative analysis of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network). This study uses selected nine independent variables to group injured people according to a dependent variable in a way that reduces variation. In this study, data on 10,536 accidents were analyzed to create risk groups for a number of complications, including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002 ~ 2004) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

목차

Abstract
1. 서론
2. 연구내용 및 방법
3. 데이터마이닝 모델
4. 분석결과
5. 결론 및 추후연구
6. 참고문헌

키워드

Data Mining CHAID C4.5 CART LR NN ROC Chart