VEHICLE CLASSIFICATION USING EVOLUTIONARY FORESTS

Murray Evans, Jonathan N. Boyle, James Ferryman

Abstract

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

@conference{icpram12,
author={Murray Evans and Jonathan N. Boyle and James Ferryman},
title={VEHICLE CLASSIFICATION USING EVOLUTIONARY FORESTS},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={387-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003763603870393},
isbn={978-989-8425-99-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - VEHICLE CLASSIFICATION USING EVOLUTIONARY FORESTS
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