Murray Evans, Jonathan N. Boyle, James Ferryman


Forests of decision trees are a popular tool for classification applications. This paper presents an approach to evolving the forest classifier, reducing the time spent designing the optimal tree depth and forest size. This is applied to the task of vehicle classification for purposes of verification against databases at security checkpoints, or accumulation of road usage statistics. The evolutionary approach to building the forest classifier is shown to out-perform a more typically grown forest and a baseline neural-network classifier for the vehicle classification task.


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Paper Citation

in Harvard Style

Evans M., N. Boyle J. and Ferryman J. (2012). VEHICLE CLASSIFICATION USING EVOLUTIONARY FORESTS . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 387-393. DOI: 10.5220/0003763603870393

in Bibtex Style

author={Murray Evans and Jonathan N. Boyle and James Ferryman},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},

in EndNote Style

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
SN - 978-989-8425-99-7
AU - Evans M.
AU - N. Boyle J.
AU - Ferryman J.
PY - 2012
SP - 387
EP - 393
DO - 10.5220/0003763603870393