A novel Euclidean quality threshold ARTMAP network and its application to pattern classification

摘要

This paper introduces a novel neural network model known as the Euclidean quality threshold ARTMAP (EQTAM) network and its application to pattern classification. The model is constructed based on fuzzy ARTMAP (FAM) and the quality threshold clustering principle. The main objective of EQTAM is to overcome the effects of training data sequences on FAM and, at the same time, to improve its classification performance. Several artificial data sets and benchmark medical data sets are used to evaluate the effectiveness of the proposed model. Performance comparisons between EQTAM and ARTMAP-based as well as other classifiers are made. From the experimental results, it can be observed that EQTAM is able to produce good results. More importantly, the performance of EQTAM is robust against the effect of training data orders or sequences.