A novel feature selection and attribute reduction based on hybrid IG-RS approach

Abstract

Document preprocessing and Feature selection are the major problem in the field of data mining, machine learning and pattern recognition. Feature Subset Selection becomes an important preprocessing part in the area of data mining. Hence, to reduce the dimensionality of the feature space, and to improve the performance, document preprocessing, feature selection and attribute reduction becomes an important parameter. To overcome the problem of document preprocessing, feature selection and attribute reduction, a theoretic framework based on hybrid Information gain-rough set (IG-RS) model is proposed. In this paper, firstly the document preprocessing is prepared; secondly an information gain is used to rank the importance of the feature. In the third stage a neighborhood rough set model is used to evaluate the lower and upper approximation value. In the fourth stage an attribute reduction algorithm based on rough set model is proposed. Experimental results show that the hybrid IG-RS model based method is more flexible to deal with documents.