A New Density-Based Method for Reducing the Amount of Training Data in k-NN Text Classification

With the rapid development of WWW, text classification has become the key technology in organizing and processing large amount of text data. As a simple, effective and nonparametric classification method, k-NN method is widely used in text classification. But k-NN clasifier not only has large computational demands, but also may decrease the precision of classification because of uneven density of training data. In this paper, a new density-based method for reducing the amount of training data is presented, which not only reduces the computational demands of k-NN classifier, but also improves the classification precision. The experiments show that the new method has better performance than the traditional k-NN method.