结合同义向量聚合和特征多类别的KNN分类算法

KNN Text Categorization Algorithm Based on Semantic-Vector-Combination and Multiclass of Feature

特征选择是文本分类的关键阶段,其选择过程将影响文本分类速度与精度。χ2统计量能很好地体现词和类别之间的关系,是文本分类领域特征提取阶段的重要方法之一。分析了χ2统计量在文本分类中的应用,发现CHI向量所表达的与各类别关系的特征词无法全面表达出此类的概念含义,依赖于训练集中出现的特征情况,且该向量仅用于特征选择阶段;针对χ2统计量特征词的表达局限及其向量没有得到充分利用的问题,提出结合同义向量聚合和特征多类别的改进KNN分类算法,该方法能够综合考虑特征所表达的含义,且通过特征集多类别矩阵使CHI向量也能在分类阶段起到提高整个算法效率的作用。实验结果与分析表明,该改进算法明显提高了文本分类效率,并且提高了分类的精度。

Feature selection is the key stage in the text categorization, and the processing of it will affect the speed and accuracy of text classification. x2 statistic is a important methods in feature selection of text categorization since it mea- sures the dependence between a term and a class effectively. Nevertheless, we found the feature in the vectors of CHI can not fully express the means of concept and it depends the training text set,and the vectors of CHI are used only for the phase of feature selection after the analysis of the application of x2 statistic in the text categorization. So this paper proposed an improved kNN text categorization algorithm based on Semantic-Vector-Combination and Multi-class of fea- ture, in which the feature considers the means of concept, and the matrix of multiclass of features will improve the effi- ciency of algorithm in the stage of categorization. The results and analysis of experiments show that the efficiency of categorization is improved and its accuracy is also enhanced.