Accuracy and Diversity in Ensemble Systems Composed of ARTMAP-Based Models

ARTMAP-based models are neural networks which uses a match-based learning procedure. The main advantage of ARTMAP-based models over error-based models, such as Multi-layer Perceptron, is the learning time, which is considered as significantly fast. This feature is extremely important in complex systems that require the use of several neural models, such as ensembles or committees, since they produce strong and fast classifiers. Aiming to add an extra contribution to ARTMAP-based ensembles, this paper presents an analysis of accuracy and diversity in these systems. As a result of this analysis, it is intended to detect any relation between these two parameters and to use this in the design of these systems.