Hybridized Rough Set Framework for Classification: An Experimental View

The proposed hybridized framework is composed of traditional Rough Set (RS) approach and classical Decision Tree (DT) induction algorithm. RS helps to identify dominant attributes and DT algorithm results in simpler, and generalized classifier. Experimental results obtained by applying the hybridized rough set framework and related base algorithms on data sets from three categories are presented in this paper. Accuracy, complexity, number of rules and number of attributes assess the performance of candidate algorithms. The results indicate that the proposed framework is effective, as a model for classification