Investigating methods for improving bagged k-NN classifiers

We experiment with bagging kNN classifiers using an optimal distance metric. The aim is to establish whether bagging kNN is useful when a better metric is used. We experiment on real world data sets, at different training set sizes. Our experiments also involve Modified bagging, which was proposed by us, to see the effect of prior knowledge on the bagging performance under the new distance measure. Results indicate the optimal metric improves the performance of bagging as well as the single classifier.