Simultaneous feature and instance selection using fuzzy-rough bireducts

Rough set theory has proven to be a useful mathematical basis for developing automated computational approaches which are able to deal with and utilise imperfect knowledge. Ever since its inception, this theory has been successfully employed for developing computationally efficient techniques for addressing problems such as the discovery of hidden patterns in data, decision rule induction, and feature selection. As an extension to this theory, fuzzy-rough sets enhance the ability to model uncertainty and vagueness more effectively. The efficacy of fuzzy-rough set based approaches for the tasks of feature selection and rule induction is now well established in the literature. Although some work has been carried out using fuzzy-rough set theory for the tasks of feature selection and instance selection in isolation, the potential of this theory for its application to tasks for the simultaneous selection of both features and instances has not been investigated thus far. This paper proposes a novel method for simultaneous instance and feature selection based on fuzzy-rough sets. The initial experimentation demonstrates that the method can significantly reduce both the number of instances and features whilst maintaining high classification accuracies.