Experiments with Safe ARTMAP and Comparisons to Other ART Networks
Mingyu Zhong, Bryan Rosander, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Mansooreh Mollaghasemi, S.M. Richie · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Fuzzy ARTMAP (FAM) is currently considered as one of the premier neural network architectures in solving classification problems. Safe ARTMAP, a modified version of FAM, was introduced to remedy the category proliferation problem that has been extensively reported in the literature. However, Safe ARTMAP's performance depends on a number of parameters. In this paper, we analyzed each parameter to set up the candidate values for evaluation. We performed an exhaustive experimentation to identify good default values for these parameters for a variety of problems, and compared the best performing Safe ARTMAP network with other best performing ART networks, including those that claim to solve the category proliferation problem.