基于后验概率制导的B-KNN文本分类方法  

B-KNN Text Categorization Method Based on Posterior Probability Guidance

作　　者：周红鹃[1] 祖永亮[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009

出　　处：《计算机工程》2011年第21期114-116,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60975034)

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。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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]