Improved GAP-RBF network for classification problems

Authors:
Runxuan Zhang;Guang-Bin Huang;N. Sundararajan;P. Saratchandran
Affiliations:
Unite of Systems Biology, Department of Genomes and Genetics, Institut Pasteur, 25-28 Rue du Dr. Roux Paris, 75724, France;School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
Venue:
Neurocomputing
Year:
2007

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Abstract

This paper presents the performance evaluation of the recently developed Growing and Pruning Radial Basis Function (GAP-RBF) algorithm for classification problems. Earlier GAP-RBF was evaluated only for function approximation problems. Improvements to GAP-RBF for enhancing its performance in both accuracy and speed are also described and the resulting algorithm is referred to as Fast GAP-RBF (FGAP-RBF). Performance comparison of FGAP-RBF algorithm with GAP-RBF and the Minimal Resource Allocation Network (MRAN) algorithm based on four benchmark classification problems, viz. Phoneme, Segment, Satimage and DNA are presented. The results indicate that FGAP-RBF produces higher classification accuracy with reduced computational complexity.