A new computational intelligence technique based on human group formation

This paper proposes a novel computational intelligence technique, based on the sociological concept of human group formation, with the aim to acquire a better solution to classification problems. The key concept of the human group formation is about the behavior of in-group members that try to unite with their own group as much as possible, and at the same time maintain social distance from the out-group members. This study compares the performance of the proposed model with that of fuzzy ARTMAP, radial basis function network, and learning vector quantization. Experimental results demonstrate the potential of the proposed approach in offering an efficient and effective solution to the problem.