基于词频信息的改进信息增益文本特征选择算法

为克服传统信息增益(IG)算法对特征项的频数考虑不足的缺陷,在对传统算法和相关改进算法深入分析的基础上,提出一种基于词频信息的改进的IG文本特征选择算法。分别从特征项在类内出现的频数、类内位置分布、不同类间的分布等方面对传统IG算法的参数进行了修正,使特征频数信息得到充分利用。对文本分类的实验结果表明,所提算法的分类精度明显高于传统IG算法和加权的IG改进算法。

Improved information gain text feature selection algorithm based on word frequency information

On the basis of elaborate analysis of traditional algorithm and relevant improved algorithms, an improved Information Gain (IG) algorithm based on word frequency information was proposed to solve the insufficient consideration of the frequency of features in traditional information gain feature selection algorithm. The improved algorithm modified parameters of the traditional IG algorithm, mainly from aspects of the frequency of features within category, distribution within category and the distribution among different categories, which can make full use of the frequency of features. The result of text categorization experiment compared with traditional IG algorithm and an improved IG algorithm of weighted indicates that the proposed algorithm has an obvious enhancement in accuracy of the text categorization.