文本分类中基于改进特征选择方法的研究

摘要

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 valida-tion.The experiment shows that this method can largely improve to the text categorization accuracy,the classification effect is greatly improved,make better classification.%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.