Learning the Classification of Traffic Accident Types
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @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