基于密度的kNN文本分类器训练样本裁剪方法

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

随着WWW的迅猛发展 ,文本分类成为处理和组织大量文档数据的关键技术 kNN方法作为一种简单、有效、非参数的分类方法 ,在文本分类中得到广泛的应用 但是这种方法计算量大 ,而且训练样本的分布不均匀会造成分类准确率的下降 针对kNN方法存在的这两个问题 ,提出了一种基于密度的kNN分类器训练样本裁剪方法 ,这种方法不仅降低了kNN方法的计算量 ,而且使训练样本的分布密度趋于均匀 ,减少了边界点处测试样本的误判 实验结果显示 。

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