Population bias control for bagging k-NN experts
Fuad M. Alkoot, Josef Kittler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
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.