Learning the Classification of Traffic Accident Types

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Beshah:2012:INCoS,

• author = "Tibebe Beshah and Dejene Ejigu and Pavel Kromer and Vaclav Snasel and Jan Platos and Ajith Abraham",
• booktitle = "4th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2012",
• title = "Learning the Classification of Traffic Accident Types",
• year = "2012",
• pages = "463--468",
• DOI = "doi:10.1109/iNCoS.2012.75",
• abstract = "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 labelling 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.",
• keywords = "genetic algorithms, genetic programming, data analysis, fuzzy set theory, learning (artificial intelligence), pattern classification, road accidents, road traffic, traffic engineering computing, evolutionary fuzzy classifier design, feature selection, machine learning, real world road accident data set, road accident data analysis, road safety improvement, symbolic classifier, traffic accident severity mitigation, traffic accident type classification, traffic management authorities, Accidents, Biological cells, Indexes, Injuries, Labeling, Vehicles, fuzzy rules, machine learning, traffic accidents",
• notes = "Also known as \cite{6337959}",

}

Genetic Programming entries for Tibebe Beshah Dejene Ejigu Pavel Kromer Vaclav Snasel Jan Platos Ajith Abraham

Citations