22 March 2001 Population bias control for bagging k-NN experts
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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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fuad M. Alkoot, Fuad M. Alkoot, Josef Kittler, Josef Kittler, } "Population bias control for bagging k-NN experts", Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421124; https://doi.org/10.1117/12.421124

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