Population bias control for bagging k-NN experts

Fuad M. Alkoot; Josef Kittler

doi:10.1117/12.421124

22 March 2001 Population bias control for bagging k-NN experts

Fuad M. Alkoot, Josef Kittler

Proceedings Volume 4385, Sensor Fusion: Architectures, Algorithms, and Applications V; (2001) https://doi.org/10.1117/12.421124
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Fuad M. Alkoot and Josef Kittler "Population bias control for bagging k-NN experts", Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); https://doi.org/10.1117/12.421124

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