Earticle

초록

영어

Many researches and analyses have been focused on industrial accidents in order to predict and reduce them. As a similar endeavor, this paper is to develop an expert system for prevention of industrial accidents. Although various previous studies have been performed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. As an initial step for the purpose of this study, this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and Answer Tree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work was chosen from 10,536 data related to manufacturing industries during three years in korea. The initial sample includes a range of different businesses including the construction and manufacturing industries, which are typically vulnerable to industrial accidents.

목차

Abstract
 1. 서론
 2. 연구방법
 3. 데이터 분류 및 분석방법
 4. 알고리즘 비교분석
 5. CHAID 알고리즘 분석 결과
 6. 전문가 시스템 개발
 7. 결론
 8. 참고문헌

키워드

저자

• 임영문 [ Leem Young Moon | 강릉대학교 산업시스템공학과 교수 ]
• 최요한 [ Choi Yo Han | 강릉대학교 산업시스템공학과 박사과정 수료 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620