Genetic Algorithm-Based Association Rule Mining Approach Towards Rule Generation of Occupational Accidents

Occupational accident is a grave issue for any industry. Therefore, proper analysis of accident data should be carried out to find out the accident patterns so that precautionary measures could be undertaken beforehand. Association rule mining (ARM) technique is mostly used in this scenario to find out the association (i.e., rules) causing accidents. But, among the rules generated by ARM, all are not useful. To handle this kind of problem, a new model ARM and genetic algorithm (GA) has been proposed in this study. The model automatically selects the optimal Support and Confidence value to generate useful rules. Out of 1285 data obtained from a steel industry in India, eleven useful rules are generated using this proposed method. The findings from this study have the potential to help the management take the better decisions to mitigate the occurrence of accidents.

[1]  Bhabesh Nath,et al.  Multi-objective rule mining using genetic algorithms , 2004, Inf. Sci..

[2]  Jhareswar Maiti,et al.  Prediction of occupational accidents using decision tree approach , 2016, 2016 IEEE Annual India Conference (INDICON).

[3]  Behrouz Minaei-Bidgoli,et al.  Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence , 2011, Expert Syst. Appl..

[4]  Jhareswar Maiti,et al.  Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports , 2014 .

[5]  Beatrice Lazzerini,et al.  A semi-supervised learning-aided evolutionary approach to occupational safety improvement , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[6]  Rajib Mall,et al.  Application of elitist multi-objective genetic algorithm for classification rule generation , 2008, Appl. Soft Comput..

[7]  Chengqi Zhang,et al.  Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support , 2009, Expert Syst. Appl..

[8]  Sou-Sen Leu,et al.  Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry , 2010 .

[9]  Qiang Meng,et al.  Tree‐Based Logistic Regression Approach for Work Zone Casualty Risk Assessment , 2013, Risk analysis : an official publication of the Society for Risk Analysis.

[10]  Tsung-Chih Wu,et al.  Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry , 2013 .

[11]  G. Guyatt,et al.  The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. , 2002, Accident; analysis and prevention.

[12]  Gianluca Bontempi,et al.  The use of intelligent data analysis techniques for system-level design: a software estimation example , 2004, Soft Comput..

[13]  Yong Bai,et al.  Development of crash-severity-index models for the measurement of work zone risk levels. , 2008, Accident; analysis and prevention.

[14]  N N Sze,et al.  Contributory factors to traffic crashes at signalized intersections in Hong Kong. , 2007, Accident; analysis and prevention.

[15]  Li-Yen Chang,et al.  Analysis of traffic injury severity: an application of non-parametric classification tree techniques. , 2006, Accident; analysis and prevention.

[16]  Donald E. Brown,et al.  Text Mining the Contributors to Rail Accidents , 2016, IEEE Transactions on Intelligent Transportation Systems.

[17]  Jhareswar Maiti,et al.  Text mining based safety risk assessment and prediction of occupational accidents in a steel plant , 2016, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).

[18]  Jhareswar Maiti,et al.  Segmented point process models for work system safety analysis , 2017 .

[19]  Jhareswar Maiti,et al.  Study of optimized SVM for incident prediction of a steel plant in India , 2016, 2016 IEEE Annual India Conference (INDICON).