Improving k-nearest neighbor efficiency for text categorization

Abstract

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Many classification methods have been applied to text categorization. The k-nearest neighbors (k-NN) is known to be one of the best state of the art classifiers when used for text categorization. However, k-NN suffers from limitations such as high computation, low tolerance to noise, and its dependency to the parameter k and distance function. In this paper, we first survey some improvements algorithms proposed in the literature to face those shortcomings. And second, we discuss an approach to improve k-NN efficiency without degrading the performance of classification. Experimental results on the 20Newsgroup and Reuters corpora show that the proposed approach increases the performance of k-NN and reduces the time classification.