An Automated Text Classification Method: Using Improved Fuzzy Set Approach for Feature Selection

A well representing feature set that has enough differentiated power plays an important role in the classification. The existing techniques for feature set selection are mostly statistical. They are not flexible to incorporate the human reasoning and the changing requirements and preferences of the real-life systems. They only make a decision between a feature inclusion or exclusion. The fuzziness of human reasoning and thinking are not considered at all that may improve the feature selection and hence the accuracy of the classifier. Also, the selection of overlapping features in case of Local Feature Selection (LFS) methods is an important issue that negatively impacts classification accuracy. For example, in case of Odd Ratio (OR), the selection may contain overlapping features of multiple classes. In this paper, a Fuzzy Set Theory (FST) based feature selection method has been proposed. The approach aims to tackle both above mentioned issues efficiently. The selected final feature set is used to train the well-known classification algorithms and the results are compared with Global Feature Selection (GFS) and LFS methods. The comparison shows that the proposed method has improved the accuracy of the classifiers and also extract comparatively small feature set that ultimately reduces the time complexity of the system.