Attribute Reduction Method Based on Improved Binary Glowworm Swarm Optimization Algorithm and Neighborhood Rough Set

Abstract

Aiming at the problems of dimension reduction and redundancy removing,an attribute reduction method based on improved binary glowworm swarm optimization algorithm and neighborhood rough set is proposed.Firstly,the population is collaborative initialization using reverse learning,and the mapping of the change function based on Sigmoid is employed for binary coding,and an improved binary glowworm opti-mization algorithm is proposed with Levy flight position update strategy.Secondly,neighborhood rough set is employed as an evaluation criterion,and the proposed algorithm is utilized as an search strategy for attribute reduction.Finally,experiments on the standard UCI datasets demonstrate the effectiveness of the attribute reduction method,and the better convergence speed and accuracy of the proposed algorithm is verified.