中文文本分类中的特征选择研究

本文介绍和比较了八种用于文本分类的特征选择方法,其中把应用于二元分类器中的优势率改造成适用于多类问题的形式,并提出了一种新的类别区分词的特征选择方法,结合两种不同的分类方法:文本相似度方法和Na?ve Bayes方法,在两个不同的数据集上分别作了训练和测试,结果表明,在这八种文本特征选择方法中,多类优势率和类别区分词方法取得了最好的选择效果。其中,当用Na?ve Bayes分类方法对各类分布严重不均的13890样本集作训练和测试时,当特征维数大于8000以后,用类别区分词作特征选择得到的宏F1值比用IG作特征选择得到的宏F1值高出3%～5%左右。

Study on Feature Selection in Chinese Text Categorization

This paper introduces and compares eight feature selection methods in text categorization. Among the eight methods , Multi-Class Odds Ratio (MC-OR) , a variant of Odds Ratio which is often used in binary classification , and a new feature selection method based on Class-Discriminating Words (CDW) are proposed. Combined with the classic VSM classifier based on cosine similarity and the Na?ve Bayes classifier , training and test are carried out on two text sets with different class distribution. As the results indicate , MC-OR and CDW gain the best selecting effect.