A Feature Analysis of Industrial Accidents Using C4.5 Algorithm

C4.5 알고리즘을 이용한 산업 재해의 특성 분석

  • Leem, Young-Moon (Department of Industrial Systems Engineering, Kangnung National University) ;
  • Kwag, Jun-Koo (Department of Industrial Systems Engineering, Kangnung National University) ;
  • Hwang, Young-Seob (Department of Industrial Systems Engineering, Kangnung National University)
  • 임영문 (강릉대학교 산업시스템공학과) ;
  • 곽준구 (강릉대학교 산업시스템공학과) ;
  • 황영섭 (강릉대학교 산업시스템공학과)
  • Published : 2005.12.31


Decision tree algorithm is one of the data mining techniques, which conducts grouping or prediction into several sub-groups from interested groups. This technique can analyze a feature of type on groups and can be used to detect differences in the type of industrial accidents. This paper uses C4.5 algorithm for the feature analysis. The data set consists of 24,887 features through data selection from total data of 25,159 taken from 2 year observation of industrial accidents in Korea For the purpose of this paper, one target value and eight independent variables are detailed by type of industrial accidents. There are 222 total tree nodes and 151 leaf nodes after grouping. This paper Provides an acceptable level of accuracy(%) and error rate(%) in order to measure tree accuracy about created trees. The objective of this paper is to analyze the efficiency of the C4.5 algorithm to classify types of industrial accidents data and thereby identify potential weak points in disaster risk grouping.


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