Application of k Nearest Neighbor on Feature Projections Classi er to Text Categorization

This paper presents the results of the application of an instance based learning algorithm k Nearest Neighbor Method on Fea ture Projections k NNFP to text categorization and compares it with k Nearest Neighbor Classi er k NN k NNFP is similar to k NN ex cept it nds the nearest neighbors according to each feature separately Then it combines these predictions using a majority voting This prop erty causes k NNFP to eliminate possible adverse e ects of irrelevant features on the classi cation accuracy Experimental evidence indicates that k NNFP is superior to k NN in terms of classi cation accuracy in the presence of irrelevant features in many real world domains