Abstract: Abstract: A hybrid classifier is presented based on the combination of rough set theory and BP neural network. Firstly, the documents are denoted by vector space model. Secondly it reduced the feature vector by using rough sets. Finally classed the documents by BP neural network. Experimental results show that the algorithm RoughANN is effective for the texts classification, and has the better performance in classification precision, stability and faulttolerance when compared with the traditional classification methods, Bayesian classifiers SVM and kNN, especially for the complex classification problems with many feature vectors.

Key words: VSM , attribute reduction, neural network, roughsets, text classification