Mining traffic accident features by evolutionary fuzzy rules

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Kromer:2013:CIVTS,
  author =       "Pavel Kromer and Tibebe Beshah and Dejene Ejigu and 
                 Vaclav Snasel and Jan Platos and Ajith Abraham",
  title =        "Mining traffic accident features by evolutionary fuzzy
                 rules",
  booktitle =    "IEEE Symposium on Computational Intelligence in
                 Vehicles and Transportation Systems, CIVTS 2013",
  year =         "2013",
  pages =        "38--43",
  address =      "Singapore",
  month =        "16-19 " # apr,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, machine
                 learning, fuzzy rules, traffic accidents, binary
                 classification, multi-class classification",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.460.4124",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.4124",
  URL =          "http://isda2002m.softcomputing.net/civts2013.pdf",
  DOI =          "doi:10.1109/CIVTS.2013.6612287",
  abstract =     "Traffic accidents represent a major problem
                 threatening peoples lives, health, and property.
                 Traffic behaviour and driving in particular is a social
                 and cultural phenomenon that exhibits significant
                 differences across countries and regions. Therefore,
                 traffic models developed in one country might not be
                 suitable for other countries. Similarly, attributes of
                 importance, dependencies, and patterns found in data
                 describing traffic in one region might not be valid for
                 other regions. All this makes traffic accident analysis
                 and modelling a task suitable for data mining and
                 machine learning approaches that develop models based
                 on actual real-world data. In this study, we
                 investigate a data set describing traffic accidents in
                 Ethiopia and use a machine learning method based on
                 artificial evolution and fuzzy systems to mine symbolic
                 description of selected features of the data set.",
  notes =        "Technical University of Ostrava

                 Also known as \cite{6612287}",
}

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

Citations