Learning the Classification of Traffic Accident Types

This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labeling of data in a real world road accident data set. The symbolic classifier was inspected in order to select i...

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Bibliographic Details
Published in2012 4th International Conference on Intelligent Networking and Collaborative Systems pp. 463 - 468
Main Authors Beshah, T., Ejigu, D., Kromer, P., Snasel, V., Platos, J., Abraham, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Accidents
Biological cells
fuzzy rules
Genetic programming
Indexes
Injuries
Labeling
machine learning
traffic accidents
Vehicles
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ISBN9781467322799
1467322792
DOI10.1109/iNCoS.2012.75

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Summary:This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labeling of data in a real world road accident data set. The symbolic classifier was inspected in order to select important features and the relations among them. Selected features provide a feedback for traffic management authorities that can exploit the knowledge to improve road safety and mitigate the severity of traffic accidents.
ISBN:9781467322799
1467322792
DOI:10.1109/iNCoS.2012.75