Title :
Gender specific classification of road accident patterns through data mining techniques
Author :
Shanthi, S. ; Ramani, R. Geetha
Author_Institution :
Dept. of Comput. Sci. & Eng., Rajalakshmi Inst. of Technol., Chennai, India
Abstract :
Road accident analysis is 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 techniques to carry out gender based classification of which RndTree and C4.5 using AdaBoost Meta classifier 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 AdaBoost used with RndTree improvised the classifier´s accuracy.
Keywords :
data mining; gender issues; learning (artificial intelligence); pattern classification; road accidents; AdaBoost meta classifier; C4.5; RndTree; University of Alabama; critical analysis reporting environment system; data mining classification; fatality analysis reporting system; gender specific classification; road accident pattern; Argon; Classification algorithms; Clustering algorithms; Data preprocessing; Sensitivity; Training; AdaBoost; Classification Algorithms; Data Mining; Meta Classifier; Road Accident Data;
Conference_Titel :
Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on
Conference_Location :
Nagapattinam, Tamil Nadu
Print_ISBN :
978-1-4673-0213-5
