关键词: 特征选择, TDF-CHI, 随机森林特征选择（RFFS）, 文本分类

Abstract: The traditional CHI algorithm ignores the term frequency of the characteristic word, and it is easy to lead to the leaking of the important feature words. Fast speed of Filter algorithm and high accuracy of Wrapper algorithm are combined in Feature selection. A feature selection algorithm that combined improved CHI（TDF-CHI） with Random Forest Feature Selection（RFFS） are proposed. Firstly, TDF-CHI is used to select the feature and remove the redundant features, by calculating the correlation between the document frequency and the category, the correlation between the term frequency and the category. And then use the RFFS algorithm to measure the importance of the remaining features to carry out the second feature selection, optimize the feature set, so that the performance of the classifier is further improved. In order to verify the superiority of the improved algorithm, it is tested on news text data which is the commonly used data in classifier algorithms. The experiments show that the improved algorithm, which can improve the accuracy and recall rate of the classifier, has better classification effect compared with the traditional CHI algorithm.

Key words: feature selection, TDF-CHI, Random Forest Feature Selection（RFFS）, text classification