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Authors: Olivier Lezoray 1 and Hubert Cardot 2

Affiliations: 1 LUSAC (EA 2607), France ; 2 Université Francois-Rabelais de Tours, Laboratoire d’Informatique (EA 2101), France

Abstract: Classifier combination constitutes an interesting approach when solving multiclass classification problems. We review standard methods used to decode the decomposition generated by a one-against-one approach. New decoding methods are proposed and are compared to standard methods. A stacking decoding is also proposed and consists in replacing the whole decoding by a trainable classifier to arbiter among the conflicting predictions of the binary classifiers. Substantial gain is obtained on all datasets used in the experiments.

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Paper citation in several formats:
Lezoray, O. and Cardot, H. (2005). Combining Multiple Pairwise Neural Networks Classifiers: A Comparative Study. In Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP, (ICINCO 2005) ISBN 972-8865-36-8, pages 52-61. DOI: 10.5220/0001193700520061

@conference{anniip05,
author={Olivier Lezoray. and Hubert Cardot.},
title={Combining Multiple Pairwise Neural Networks Classifiers: A Comparative Study},
booktitle={Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP, (ICINCO 2005)},
year={2005},
pages={52-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001193700520061},
isbn={972-8865-36-8},
}

TY - CONF

JO - Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP, (ICINCO 2005)
TI - Combining Multiple Pairwise Neural Networks Classifiers: A Comparative Study
SN - 972-8865-36-8
AU - Lezoray, O.
AU - Cardot, H.
PY - 2005
SP - 52
EP - 61
DO - 10.5220/0001193700520061

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