Class information feature selection method for text classification

With the explosion of web documents,text classification becomes more important in Information Retrieval applications.It is very difficult to evaluate the statistical characteristics of samples because of the high dimensions.It will lead to "over study" and reduce classifiers' performance.So that feature selection and extraction before analysis are necessary.A class information feature selection method is proposed,in which the class information of the training document is taken into account while keeping as much document information as possible.The experiments show that this method can get good performance,and it is consistently better than OCFS and CHI on macro average F_1.