Efficient KNN Text Categorization Based on Multiedit and Condensing Techniques

As a simple and effective classification approach, KNN is widely used in text categorization. However, KNN classifier not only has the large computational and store requirements, but also deteriorates performance of classification because of uneven distribution of training data. In this paper, we present a combinational technique, multi-edit-nearest-neighbor and condensing techniques, for reducing the noises of training data and decreasing the cost of time and space. Our experiment results illustrate that this strategy can solve above problems effectively.