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Description
  • Traffic accidents represent a major problem threatening peoples lives, health, and property. Traffic behavior 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.
  • Traffic accidents represent a major problem threatening peoples lives, health, and property. Traffic behavior 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. (en)
Title
  • Mining traffic accident features by evolutionary fuzzy rules
  • Mining traffic accident features by evolutionary fuzzy rules (en)
skos:prefLabel
  • Mining traffic accident features by evolutionary fuzzy rules
  • Mining traffic accident features by evolutionary fuzzy rules (en)
skos:notation
  • RIV/61989100:27740/13:86088868!RIV14-MSM-27740___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), P(EE.2.3.20.0073)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 88489
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27740/13:86088868
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • traffic accidents; multi-class classification; machine learning; genetic programming; fuzzy rules; binary classification (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9A33E8947DA4]
http://linked.open...v/mistoKonaniAkce
  • Singapur
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, CIVTS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Abraham Padath, Ajith
  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
  • Beshah, Tibebe
  • Ejigu, Dejene
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/CIVTS.2013.6612287
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-1-4673-5913-9
http://localhost/t...ganizacniJednotka
  • 27740
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