Variable selection and ranking for analyzing automobile traffic accident data

Proceedings of the ACM Symposium on Applied Computing

Volumn 1, Issue , 2005, Pages 32-37

  Wang, Huanjing ^a   Parrish, Allen ^a   Smith, Randy K ^a   Vrbsky, Susan ^a  

^a University of Alabama   (United States)

Author keywords

Decision tree; Traffic accident data; Variable and feature selection; Variable ranking

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA MINING; DATABASE SYSTEMS; DECISION THEORY; LEARNING SYSTEMS;

TRAFFIC ACCIDENT DATA; VARIABLE AND FEATURE SELECTION; VARIABLE RANKING;

TRAFFIC CONTROL;

EID: 33644550281     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1066677.1066688     Document Type: Conference Paper

References (22)

1. M. A. Hall and L. A. Smith. Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In Proceedings of the Florida Artificial Intelligence Symposium, 1999.
2. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182, 2003.
3. R. Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2): 273-324, December 1997.
4. A. Parrish, B. Dixon, D. Cordes, S. Vrbsky and D. Brown. CARE: A tool to analyze automobile crash data. IEEE Computer, Vol. 36, No. 6, pp. 22-30, June 2003.
5. D. Mladenic and M. Grobelnik. Feature selection for unbalanced class distribution and naive bayes. In Proceedings of the 16th International Conference on Machine Learning, pp. 258-267, 1999.
6. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46:389-422, 2002.
7. T. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer and D. Haussler. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16:906-914, 2000.
8. T. R. Golub et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:531-537, October 1999.
9. D. W. Stockburger. Introductory statistics: Concepts, Models, and Applications.
10. D. Slonim, P. Tamayo, J. Mesirov, T. Golub and E. Lander. Class prediction and discovery using gene expression data. In Proceedings of 4th Annual International Conference on Computational Molecular Biology Universal Academy Press, Tokyo, Japan, pp. 263-272, 2000.
11. J. Weston, A. Elisseff, B. Scholkopf, and M. Tipping. Use of the zero - norm with linear models and kernel methods. Journal of Machine Learning Research, 3:1439-1461, 2003.
12. P. Pavlidis, J. Weston, J. Cai, and W. N. Grundy. Learning gene functional classifications from multiple data types. Journal of Computational Biology, 9(2): 401-411, 2002.
13. C. Bishop. Neural networks for pattern recognition. Oxford University Press, 1995.
14. G. Forman. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3:1289-1305, 2003.
15. W. Hawley. Foundations of statistics. Harcourt Brace & Company, 1996.
16. R. Zembowicz and J. Zytkow. From contingency tables to various forms of knowledge in databases. Advances in Knowledge Discovery and Data Mining, pp. 328-349, AAAI Press/The MIT Press, 1996.
17. M. Grimaldi, P. Cunningham and A. Kokaram. An evaluation of alternative feature selection strategies and ensemble techniques for classifying music. The 14th European Conference on Machine Learning and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Dubrovnik, Croatia 2003.
18. Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning, pp. 412-420, 1997.
19. http://clopinet.com/isabelle/Projects/NIPS2003/.
20. J. R. Quinlan. C4.5: programs for machine learning. Los Altos, California: Morgan Kaufmann, 1993.
21. H. Stoppiglia, G. Dreyfus, R. Dubois and Y. Oussar. Ranking a random feature for variable and feature selection. Journal of Machine Learning Research, 3:1399-1414, 2003.
22. G. John, R. Kohavi and K. Pfleeger. Irrelevant features and the subset selection problem. In Proceedings of the 11th International Conference on Machine Learning, pp. 121-129, 1994.