Fuzzy ARTMAP (FAM) is currently considered as one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is, Fuzzy ARTMAP has the tendency of increasing its network size as it is confronted with more and more data, especially if the dat...

ARTMAP-FD extends fuzzy A R T M A P t o perform famaliarity discrimination. That is , the network learns to abstain from meaningless guesses on patterns not belonging to a class represented in the training set. ARTMAP-FD can also be applied in conjunction with sequential evidence accumulation. Its performance is illustrated here on simulated radar range profile data .

An important task for a direct mailing company is to detect potential customers in order to avoid unnecessary and unwanted mailing. This paper describes a non-linear method to predict profiles of potential customers using dARTMAP, ARTMAP-IC, and Fuzzy ARTMAP neural networks. The paper discusses advantages of the proposed approaches over similar techniques based on MLP neural networks.

Protein sequence classification involves feature selection for accurate classification. Popular protein sequence classification techniques involve extraction of specific features from the sequences. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP, Rough Set Classifier etc for accurate classification. This paper presents a review ...

Fuzzy ARTMAP is compared to a classifier system (CS) called PASS (predictive adaptive sequential system). Previously reported results in a benchmark classification task suggest that Fuzzy ARTMAP systems perform better and are more parsimonious than systems based on the CS architecture. The tasks considered here differ from ordinary classificatory tasks in the amount of output uncertainty associ...

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due their plasticity-stability capability and property. An imbalanced dataset occurs when there is the presence of one class containing a greater number instances than other classes. It skewed representation data. Many standard algorithms have failed in mitigating problems. There f...

We introduce Ellipsoid-ART (EA) and Ellipsoid-ARTMAP (EAM) as a generalization of Hyper-sphere ART (HA) and Hypersphere-ARTMAP (HAM) respectively. As was the case with HMHAM, these novel architectures are based on ideas rooted in Fuzzy-ART (FA) and Fuuy-ARTMAP (FAM). While FMFAM aggregate input data using hyperrectangles, EA/EAM utilize hyper-ellipsoids for the same purpose. Due to their learni...