Comparing neural networks: A benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP

This article compares the performance of some recently developed incremental neural networks with the well-known multilayer perceptron (MLP) on real-world data. The incremental networks are fuzzy ARTMAP (TAM), growing neural gas (GNG) and growing cell structures (GCS), The real-world datasets consist of four different datasets posing different challenges to the networks in terms of complexity of decision boundaries, overlapping between classes, and size of the datasets, The performance of the networks on the datasets is reported with respect to measure classification error, number of training epochs, and sensitivity toward variation of parameters. Statistical evaluations are applied to examine the significance of the results. The overall performance ranks in the following descending order: GNG, GCS, MLP, FAM.