Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification

Nearest neighbor (NN) classifier is a popular non-parametric classifier. It is conceptually a simple classifier and shows good performance. Due to the curse of dimensionality effect, the size of training set needed by it to achieve a given classification accuracy becomes prohibitively large when the dimensionality of the data is high. Generating artificial patterns can reduce this effect. In this paper, we propose a novel pattern synthesis method called partition based pattern synthesis which can generate an artificial training set of exponential order when compared with that of the given original training set. We also propose suitable faster NN based methods to work with the synthetic training patterns. Theoretically, the relationship between our methods and conventional NN methods is established. The computational requirements of our methods are also theoretically established. Experimental results show that NN based classifiers with synthetic training set can outperform conventional NN classifiers and some other related classifiers.