A NEW DENSITY-BASED METHOD FOR REDUCING THE AMOUNT OF TRAINING DATA IN K-NN TEXT CLASSIFICATION

摘要

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.