A modified electromagnetic-like mechanism for rough set attribute reduction

Abstract

Reducing redundant attributes is the important issue in classification of data and knowledge discovery. This paper investigates a modified and adapted continuous optimization algorithm to solve a discrete optimization problem. To achieve this aim, a modified electromagnetic-like mechanism (MEM) is adapted to find the minimal attribute based on rough set for the first time. The procedure of MEM works based on the attraction-repulsion mechanism of electromagnetic theory, it memorizes and utilizes histories of the charges and the locations of points and the procedure also is able to escape from local optimal solutions. The MEM is adapted by a new discretization function and tested on well-known UCI datasets. Its experimental results show that proposed algorithm is able to find acceptable results when compared with the general draft of EM, GA and PSO algorithms.