- 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 = "
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}",
