논문 상세보기
pp.94-100
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.
1. 서론
2. 연구내용 및 방법
3. 데이터마이닝 모델
3.1 Decision Tree
3.2 Logistic Regression
3.3 신경망(Neural Network)
4. 분석결과
4.1 변수 선택
4.2 모델별 결과 비교
5. 결론 및 추후연구
5. 참고문헌
