LAUSR

Nowadays the incredibly advanced developments in information technologies have led to exponential growth in the datasets with respect to both the dimensionality and the sample size. This trend can be easily illustrated in popular online data repositories (e.g., UCI machine learning repository). The more growth in the datasets, the more challenging the data management becomes. This is because such datasets usually comprise a high level of noise as well as the necessary information. Therefore, the elimination of noisy features in the datasets is an important task to discover meaningful knowledge. Although a large number of feature selection approaches have been proposed in the literature to deal with noisy features, the need for the studies based on feature selection has not come to an end. In this paper, we propose differential evolution-based feature selection approaches wrapped around the principles of fuzzy rough set theory. In contrast to well-known feature selection criteria such as standard mutual information, standard rough set and probabilistic rough set, our approaches can also deal with real-valued variables without the requirement of discretization. Moreover, the feature subsets selected by our approaches can profoundly improve the classification performance compared to the recent particle swarm optimization approaches based on probabilistic rough set and the state-of-the-art filter approaches.