An Approach to Reduce the Computational Burden of Nearest Neighbor Classifier

Nearest Neighbor Classifiers demand high computational resources i.e, time and memory. Reducing of reference set(training set) and feature selection are two different approaches to this problem. This paper presents a method to reduce the training set both in cardinality and dimensionality in cascade. The experiments are done on several bench mark datasets and the results obtained are satisfactory.