An efficient sequential RBF network for bio-medical classification problems

GAP-RBF (growing and pruning RBF) algorithm is a newly developed sequential growing and pruning algorithm for RBF networks for function approximation problems. It has been confirmed to produce excellent performance for problems in function approximation area, but its performance for classification problems has not been evaluated yet. In this paper, the performance of GAP-RBF for bio-medical classification problems is investigated. Its classification performance is compared with the conventional multilayer feed forward network (MFN) and a well-known sequential learning algorithm-minimal resource allocation network (MRAN) based on two benchmark problems from the bio-medical classification area from PROBEN1 database. The results indicate that GAP-RBF/ algorithm can achieve a higher or at least similar classification accuracy with a more compact network structure and faster learning speed. Some limitations of this algorithm for classification problems are also identified.