基于粗糙集与改进KNN算法的文本分类方法的研究

摘要

KNN算法是文本自动分类领域中的一种常用算法,对于低维度的文本分类,其分类准确率较高.然而在处理大量高维度文本时,传统KNN算法由于需处理大量训练样本导致样本相似度的计算量增加,降低了分类效率.为解决相关问题,本文首先利用粗糙集对高维文本信息进行属性约简,删除冗余属性,而后用改进的基于簇的KNN算法进行文本分类.通过仿真实验,证明该方法能够提高文本的分类精度和准确率.%The KNN algorithm is a common method in the field of automatic text classification. It has high classification accuracy for texts with low dimensional vectors. However, when it deals with large numbers of high-dimensional texts, the traditional KNN algorithm, due to the need to process considerable the training samples, result in increased similarity calculation and reduced classification efficiency. To solve ensuing problems, this paper uses the rough set method to reduce the attributes of decision table and remove redundant attributes, and then the improved cluster-based KNN algorithm is used to classify texts. Simulation results show that the method can improve the precision and accuracy rate of text classification.