关键词: 文本表示, 向量空间模型, 特征选择, TFIDF

Abstract: Text representation has been the fundamental problem in Information Retrieval.tf.idf（term frequency，inverse document frequency） as one of term weighting schemes in Vector Space Model is a good text representation，Which is popular and make good results in the field of Information Retrieval.The difference of the proportion of distribution of terms in text collection is one of the most important factors of expressing the content of text.But the calculation of IDF，don’t consider the information of distribution about terms among classes，and don’t consider the more term weighting for the terms of the relative distributed balance inner classes.The improved TFIDF are used to select feature，KNN algorithm and genetic algorithm are used to train the classifier.and proves that the improved TFIDF method is feasible.

Key words: text representation, Vector Space Model, feature selection, TFIDF