Data classification using fuzzy-GSA
Hossein Askari, Seyed-Hamid Zahiri · 2011
An intelligent gravitational search algorithm (IGSA) is introduced to develop a novel classifier. The proposed method is called IGSA-classifier. At first, a fuzzy controller is designed for intelligently controlling the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers.