Evaluation of discernibility matrix based reduct computation techniques

Rough set theory provides some principles that are used for data classification and knowledge reduction. Reduct is one of the main concepts that can be used for feature set reduction and for data classification. Finding the reduct set is computationally expensive for data sets with large number of attributes. Several heuristic approached have been proposed to extract reduct sets where some of the approached used the Discernibility Matrix (DM) concept to perform the reduct computation. In this paper the Johnson reduction algorithm and the Object Reduct using Attribute Weighting technique algorithm (ORAW) for reduct computation are evaluated. The two approaches aim at reducing the number of features in the dataset. To evaluate the two approaches several UCI standard datasets were used in the experiments. The results of the experiments showed that the ORAW approach gives better results in term of classification accuracy where the average classification accuracy over eight data sets achieved by the ORAW approach was 85.6%; while Johnson approach achieved 78.8% of accuracy. For further evaluation, the two approaches were compared with some other well known classification techniques.