BeeRBF: A bee-inspired data clustering approach to design RBF neural network classifiers

Different methods have been used to train radial basis function (RBF) neural networks. This paper proposes the use of a bee-inspired algorithm, named cOptBees, plus a heuristic to automatically select the number, location and dispersions of basis functions to be used in RBF networks. cOptBees was originally designed to solve data clustering problems and the prototypes determined by the algorithm will be selected as the centers for the RBF network. The presented approach, named BeeRBF, is used to solve classification problems and is evaluated both in terms of the decision boundaries generated and classification accuracy. The performance of BeeRBF was compared with that of k-means, random center selection and some other proposals from the literature. The results show that BeeRBF is competitive and has the advantage of automatically determining the number of centers to be used in the RBF network.