Parallel Feature Selection Algorithm based on Rough Sets and Particle Swarm Optimization
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 43–50 (2014)
Abstract. The aim of this paper is to propose a new method of solving feature selection problem. Foundations of presented algorithm lie in the theory of rough sets. Feature selection methods based on rough sets have been used with success in many data mining problems, but their weakness is their computational complexity. In order to overcome the above-mentioned problem, researches used diverse approximation techniques. This paper presents a new approach to approximation of reducts.