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

fYear

2012

fDate

30-31 March 2012

Firstpage

359

Lastpage

365

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;

fLanguage

English

Publisher

ieee

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

Type

conf

Filename

6215625