Heuristic search for fuzzy-rough bireducts and its use in classifier ensembles

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. Fuzzy-rough set theory is an extension to rough set theory and enhances the ability to model uncertainty and vagueness more effectively. There have been many developments in this area which offer robust methods for feature selection or instance selection. However, these are often carried out in isolation rather than considering both types of selection simultaneously. For this purpose, the notion of a bireduct has been proposed recently but the task of finding bireducts of high quality remains a significant challenge. This paper presents a heuristic strategy for the identification of fuzzy-rough bireducts, which is based on a music-inspired global optimisation algorithm called harmony search. The concept of e-bireducts is employed in this approach for the evaluation and improvisation of the candidate solutions. The stochastically-selected bireducts are also utilised to construct classifier ensembles. The presented technique is experimentally evaluated using a number of real-valued benchmark data sets.