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Proceedings Paper

Population bias control for bagging k-NN experts
Author(s): Fuad M. Alkoot; Josef Kittler
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Paper Abstract

We investigate bagging of k - NN classifiers under varying set sizes. For certain set sizes bagging often under-performs due to population bias. We propose a modification to the standard bagging method designed to avoid population bias. The modification leads to substantial performance gains, especially under very small sample size conditions. The choice of the modification method used depends on whether prior knowledge exists or not. If no prior knowledge exists then insuring that all classes exist in the bootstrap set yields the best results.

Paper Details

Date Published: 22 March 2001
PDF: 11 pages
Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421124
Show Author Affiliations
Fuad M. Alkoot, Univ. of Surrey (Kuwait)
Josef Kittler, Univ. of Surrey (United Kingdom)

Published in SPIE Proceedings Vol. 4385:
Sensor Fusion: Architectures, Algorithms, and Applications V
Belur V. Dasarathy, Editor(s)

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