摘要： 针对K最近邻(KNN)方法分类准确率高但分类效率较低的特点，提出基于后验概率制导的贝叶斯K最近邻(B-KNN)方法。利用测试文本的后验概率信息对训练集多路静态搜索树进行剪枝，在被压缩的候选类型空间内查找样本的K个最近邻，从而在保证分类准确率的同时提高KNN方法的效率。实验结果表明，与KNN相比，B-KNN的性能有较大提升，更适用于具有较深层次类型空间的文本分类应用。

关键词: 文本分类, 后验概率, 贝叶斯分类器, K最近邻方法, 贝叶斯K最近邻方法

Abstract: Considering K Nearest Neighbor(KNN) method has high accuracy but poor efficiency, this paper proposes a text categorization method based on the guidance of posterior probability named B-KNN. By using the posterior probabilities collected from the training text, B-KNN prunes the multi-branch-static-searching tree of the training dataset and reduces the candidate class set where K nearest neighbors can be found so that the efficiency of KNN method can be improved while preserving its classification accuracy. Experimental results show that B-KNN method remarkably outperforms KNN method, and it is more suitable for classification tasks with deep hierarchy categorization space.

Key words: text categorization, posterior probability, Bayesian classifier, K Nearest Neighbor(KNN) method, B-KNN method

中图分类号: 

• TP391