The proposed algorithm for feature selection based on rough set and mutual information

The feature selection approaches based on rough set theory aim to reduce the input data for improvement classification accuracy. Most existing approaches have concerned the discernibility relation to find the features, and have employed the dependency function for measuring the goodness of feature. The most relevant information cannot be visible by using information from discernibility relation only, so that neglecting indiscernibility relation, mostly relevant may be invisible. Moreover, their results are not tolerant to noisy or inconsistency data. Therefore, this paper proposes new algorithm based on rough set theory, which concerned both the discernibility and indiscernibility relations. The experimental results show that our approach gives higher classification accuracy than existing approaches. © 2014 Pushpa Publishing House, Allahabad, India.