Abstract
Rich developing countries suffer from the consequences of increase in both human and vehicle population. Road accident fatality rates depend upon many factors which could vary for different countries. It is a very challenging task and investigating the dependencies between the attributes become complex because of many environmental and road related factors. In this research work we applied data mining classification technique RndTree and RndTree using ensemble methods viz. Bagging, AdaBoost and Multi Cost Sensitive Bagging (MCSB) to carry out vehicle safety device based classification of which RndTree using Adaboost gives high accurate results. The training dataset used for the research work is obtained from Fatality Analysis Reporting System (FARS) which is provided by the University of Alabama’s Critical Analysis Reporting Environment (CARE) system. The results reveal that RndTree using Adaboost improvised the classifier’s accuracy.
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Shanthi, S., Geetha Ramani, R. (2013). Vehicle Safety Device (Airbag) Specific Classification of Road Traffic Accident Patterns through Data Mining Techniques. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_45
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DOI: https://doi.org/10.1007/978-3-642-31552-7_45
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