Analysis on classification performance of rough set based reducts

Feature subset selection and data reduction is a fundamental and most explored area in machine learning and data mining. Rough set theory has been witnessed great success in attribute reduction. A series of reduction algorithms were constructed for all kinds of applications based on rough set models. There is usually more than one reduct for some real world data sets. It is not very clear which one or which subset of the reducts should be selected for learning. Neither experimental comparison nor theoretic analysis was reported so far. In this paper, we will review the proposed attribute reduction algorithms and reduction selection strategies. Then a series of numeric experiments are presented. The results show that, statistically speaking, the classification systems trained with the reduct with the least features get the best generalization power in terms of single classifiers. Furthermore, Good performance is observed from combining the classifiers constructed with multiple reducts compared with Bagging and random subspace ensembles.