Variable selection and ranking for analyzing automobile traffic accident data

Authors:
Huanjing Wang;Allen Parrish;Randy K. Smith;Susan Vrbsky
Affiliations:
The University of Alabama, Tuscaloosa, AL;The University of Alabama, Tuscaloosa, AL;The University of Alabama, Tuscaloosa, AL;The University of Alabama, Tuscaloosa, AL
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

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Abstract

Variable ranking and feature selection are important concepts in data mining and machine learning. This paper introduces a new variable ranking technique named Sum Max Gain Ratio (SMGR). The new technique is evaluated within the domain of traffic accident data and against a more generalized dataset. In certain cases, SMGR is empirically shown to provide similar results to established approaches with significantly better runtime performance.