A prototype optimization method for nearest neighbor classification by gravitational search algorithm
Mohadeseh Rezaei, Hossein Nezamabadi–pour · 2014
In recent years, many efforts have been done to solve clustering and classification problems by using heuristic algorithms. In this paper, gravitational search algorithm (GSA) which is one of the newest swarm based heuristic search technique, is employed to generate prototypes for nearest-neighbor (NN) classification. The proposed method is compared with several state-of-the-art techniques and results are presented. The comparison shows that our proposed method can achieve higher classification accuracy than the competing methods and has a good performance in the field of prototype generation.