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Classification of Vehicle Collision Patterns in Road Accidents using Data Mining Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 35 - Number 12 |
| Year of Publication: 2011 |
| Authors: S. Shanthi, Dr. R. Geetha Ramani |
10.5120/4542-6455
|
S. Shanthi, Dr. R. Geetha Ramani . Classification of Vehicle Collision Patterns in Road Accidents using Data Mining Algorithms. International Journal of Computer Applications. 35, 12 ( December 2011), 30-37. DOI=10.5120/4542-6455
Abstract
This paper emphasizes the importance of Data Mining classification algorithms in predicting the vehicle collision patterns occurred in training accident data set. This paper is aimed at deriving classification rules which can be used for the prediction of manner of collision. The classification algorithms viz. C4.5, C-RT, CS-MC4, Decision List, ID3, Naïve Bayes and RndTree have been applied in predicting vehicle collision patterns. The road accident training data set obtained from the Fatality Analysis Reporting System (FARS) which is available in the University of Alabama’s Critical Analysis Reporting Environment (CARE) system. The experimental results indicate that RndTree classification algorithm achieved better accuracy than other algorithms in classifying the manner of collision which increases fatality rate in road accidents. Also the feature selection algorithms including CFS, FCBF, Feature Ranking, MIFS and MODTree have been explored to improve the classifier accuracy. The result shows that the Feature Ranking method significantly improved the accuracy of the classifiers.
References
- Andreas G.K., Janecek, Wilfried N. Gansterer, Michael A. Demel Michael, Gerhard F. Ecker, “On the Relationship Between Feature Selection and Classification Accuracy”, 2008, JMLR: Workshop and Conference Proceedings, pp.90-105.
- Chang L. and H. Wang, "Analysis of traffic injury severity: An application of non-parametric classification tree techniques Accident analysis and prevention", 2006, Accident analysis and prevention, Vol. 38(5), pp 1019-1027.
- Han, J. and Kamber, M., “Data Mining: Concepts and Techniques”, Academic Press, ISBN 1- 55860-489-8.
- Handan Ankarali Camdeviren, Ayse Canan Yazici, Zeki Akkus, Resul Bugdayci, Mehmet Ali Sungur, “A Comparison of logistic regression model and classification tree: An application to postpartum depression data”, 2007, Expert Systems with Applications, Vol. 32 ,pp. 987–994.
- I-Cheng Yeh, Che-hui Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients”, Expert Systems with Applications, 2009, Vol.36, pp. 2473–2480.
- Isabelle Guyon, Andr´e Elisseeff, “An Introduction to variable and Feature Selection”, Journal of Machine Learning Research, 2003, Vol. 3, pp. 1157-1182.
- Lei Yu, Huan Liu, “Feature Selection for high-Dimensional Data: A Fast Correlation-Based Filter Solution”, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.
- Mark A. Hall, "Correlation Based Feature Selection for Machine Learning”, Ph.D. Thesis, Department of Computer Science, Waikato University, Hamilton, NZ, 1999.
- Nojun Kwak and Chong-Ho Choi , “Input Feature Selection for Classification Problems”, IEEE Transactions On Neural Networks, Vol. 13, No. 1, January 2002.
- Weimin Chen , Chaoqun Ma, Lin Ma , “Mining the customer credit using hybrid support vector machine technique”, Expert Systems with Applications, 2009, Vol. 36, pp. 7611–7616.
- Yong Soo Kim, “Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size”, Expert Systems with Applications, 2008, Vol. 34, pp. 1227–1234.
- Tanagra Data Mining tutorials, http://data-mining-tutorials.blogspot.com
- www.nhtsa.gov – FARS Analytic Reference Guide.
- World Health Organization, Global status report on road safety: time for action, Geneva, 2009.
- Feature Selection Algorithm, http://featureselection.asu.edu
- J. Rose Quinlan, “Programs for machine learning”.
- Building Classification Models ID3 and C4.5, http://www.cis.temple.edu/~ingargio/cis587/readings/id3-C4.5.html
- Random Tree Algorithm, http://www.answers.com
- Classification Algorithms, http://www.statsoft.com
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