An Iterative Hybrid Filter-Wrapper Approach to Feature Selection for Document Clustering

The manipulation of large-scale document data sets often involves the processing of a wealth of features that correspond with the available terms in the document space. The employment of all these features in the learning machine of interest is time consuming and at times reduces the performance of the learning machine. The feature space may consist of many redundant or non-discriminant features; therefore, feature selection techniques have been widely used. In this paper, we introduce a hybrid feature selection algorithm that selects features by applying both filter and wrapper methods in a hybrid manner, and iteratively selects the most competent set of features with an expectation maximization based algorithm. The proposed method employs a greedy algorithm for feature selection in each step. The method has been tested on various data sets whose results have been reported in this paper. The performance of the method both in terms of accuracy and Normalized Mutual Information is promising.