Browse > Article

Feature Analysis on Industrial Accidents of Manufacturing Businesses Using QUEST Algorithm  

Leem, Young-Moon (Industrial Systems Engineering, Kangnung National University)
Rogers, K.J. (Industrial & Manufacturing Systems Engineering, The University of Texas at Arlington)
Hwang, Young-Seob (Industrial Systems Engineering, Kangnung National University)
Publication Information
International Journal of Safety / v.5, no.1, 2006 , pp. 37-41 More about this Journal
Abstract
The major objective of the statistical analysis about industrial accidents is to determine the safety factors so that it is possible to prevent or decrease the number of future accidents by educating those who work in a given industrial field in safety management. So far, however, there exists no quantitative method for evaluating danger related to industrial accidents. Therefore, as a method for developing quantitative evaluation technique, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze features of industrial accidents, a retrospective analysis was performed on 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work was chosen from data related to manufacturing businesses during a three-year period ($2002{\sim}2004$) in Korea. This study used AnswerTree of SPSS and the analysis results enabled us to determine the most important variables that can affect injured people such as the occurrence type, the company size, and the time of occurrence. Also, it was found that the classification system adopted in the present study using QUEST algorithm is quite reliable.
Keywords
cross-validation; gains chart; quest; testing data; training data;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. J. Berry, G. S, Linoff, Mastering data mining: The art and science of customer relationship management, New York: John Wiley & Sons, 2000
2 P. A. Chou, 'Optimal partitioning for classification and regression trees', IEEE Transactions on Pattern Analysis and machine Intelligence, 12, 340-354, 1991
3 J. A. Michael, S. L. Gordon, 'Data mining technique: for marketing, sales and customer support', New York: Wiley, 1997
4 S. M. Weiss, C. A. Kulikowski, Computer systems that learn, Morgan Kaufmann, San Mateo, CA, USA, 1991
5 Jinlu Kuang, Soonhie Tan, 'GPS-based attitude determination of gyrostat satellite by quaternion estimation algorithms', Acta Astronautica, Vol. 51, No. 11, pp. 743-759, 2002   DOI   ScienceOn
6 S. Y. Shon, Tae Hee Moon, 'Decision Tree Based on Data Envelopment Analysis for Effective Technology Commercialization', Expert Systems with Applications, 26, pp. 279-284, 2004   DOI   ScienceOn
7 K. J. Hunt, 'Classification by induction: application to modeling and control of non-linear dynamical systems', Intelligent Systems Engineering, 24, pp. 231-245, 1993
8 S. H. Ho, S. H. Jee, J. E. Lee, J. S. Park, 'Analysis on risk factors for cervical cancer using induction technique', Expert Systems with Applications, 27, pp. 97-105, 2004   DOI   ScienceOn
9 R. Kohavi, Wrappers for Performance Enhancement and Oblivious Decision Graphs, Ph.D. Thesis, University of Stanford, USA, 1995a
10 Y. L. Chen, C. L. Hsu, S. C. Chou, 'Constructing a multi-valued and multi-labeled decision tree', Expert Systems with Applications, 25(2), pp. 199-209, 2003   DOI   ScienceOn
11 J. Bala, 'Using learning to facilitate the evolution of features for recognizing visual concepts', Evoltionary Computation, Vol. 4, pp. 297-312, 1996   DOI   ScienceOn
12 R. Kohavi, 'A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection', Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, Morgan Kaufmann, San Francisco, CA, USA, 1995b
13 W. Y. Loh, Y. S. Shih, 'Split selection methods for classification trees', Statistica Sinica, 7, pp. 815-840, 1997