Fast text classification: a training-corpus pruning based approach

Abstract

With the rapid growth of on-line information available, text classification is becoming more and more important. kNN is a widely used text classification method of high performance. However, this method is inefficient because it requires a large amount of computation for evaluating the similarity between a test document and each training document. In this paper, we propose a fast kNN text classification approach based on pruning the training corpus. By using this approach, the size of training corpus can be condensed sharply so that time-consuming on kNN searching can be cut off significantly, and consequently classification efficiency can be improved substantially while classification performance is preserved comparable to that of without pruning. Effective, algorithm for text corpus pruning is designed. Experiments over the Reuters corpus are carried out, which validate the practicability of the proposed approach. Our approach is especially suitable for on-line text classification applications.