文本分类中基于改进特征选择方法的研究

Text Categorization Based on Improved Feature Selection in Text Categorization

文本分类不仅可以提高分类的效率,而且可使人们更快地找到想要获取的信息。在特征选择方法的基础上,分析了卡方统计法的缺点,对其提出了一种改进的方法,同时采用支持向量机分类的算法和词频-逆向文件频率权重函数对其进行了验证。通过实验得出此方法可以在很大程度上提高文本分类精确度,使分类的效果更好。

Text categorization not only can improve the efficiency of categorization,but also can make people quickly find the information they want.On the basis of the feature selection method,this paper analyzes Chi-square（CHI）statistical method shortcomings,and proposes a Chi-square statistical method.At the same time,the Support Vector Machine（SVM）classification’s algorithm and Term Frequency-Inverse Document Frequency（TF-IDF）weight function are used on the validation.The experiment shows that this method can largely improve to the text categorization accuracy,the classification effect is greatly improved,make better classification.