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Authors: Amer. J. AlBaghdadi and Fuad M. Alkoot

Affiliation: Telecommunication and Navigation Institute, Kuwait

Abstract: An experimental evaluation of Bagging K-nearest neighbor classifiers (KNN) is performed. The goal is to investigate whether varying soft methods of aggregation would yield better results than Sum and Vote. We evaluate the performance of Sum, Product, MProduct, Minimum, Maximum, Median and Vote under varying parameters. The results over different training set sizes show minor improvement due to combining using Sum and MProduct. At very small sample size no improvement is achieved from bagging KNN classifiers. While Minimum and Maximum do not improve at almost any training set size, Vote and Median showed an improvement when larger training set sizes were tested. Reducing the number of features at large training set size improved the performance of the leading fusion strategies.

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Paper citation in several formats:
J. AlBaghdadi, A. and M. Alkoot, F. (2005). Bagging KNN Classifiers using Different Expert Fusion Strategies. In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS; ISBN 972-8865-28-7, SciTePress, pages 219-224. DOI: 10.5220/0002572002190224

@conference{pris05,
author={Amer. {J. AlBaghdadi}. and Fuad {M. Alkoot}.},
title={Bagging KNN Classifiers using Different Expert Fusion Strategies},
booktitle={Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS},
year={2005},
pages={219-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002572002190224},
isbn={972-8865-28-7},
}

TY - CONF

JO - Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS
TI - Bagging KNN Classifiers using Different Expert Fusion Strategies
SN - 972-8865-28-7
AU - J. AlBaghdadi, A.
AU - M. Alkoot, F.
PY - 2005
SP - 219
EP - 224
DO - 10.5220/0002572002190224
PB - SciTePress