A Density-Based Method for Reducing the Amount of Training Data in kNN Text Classification

With the rapid development of World Wide Web, text classification has become the key technology in organizing and processing large amount of document data As a simple, effective and nonparametric classification method, k NN method is widely used in document classification But k NN classifier not only has large computational demands, but also may decrease the precision of classification because of the uneven density of training data In this paper, a density based method for reducing the amount of training data is presented, which solves two problems mentioned above It not only reduces the computational demands of k NN classifier, but also makes the density of training data even and decreases the wrong classification between the edge of classes The experiment also shows that it has good performance