Managing category proliferation in fuzzy ARTMAP caused by overlapping classes
IEEE Transactions on Neural Networks
C++ implementation of neural networks trainer
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Improved computation for Levenberg-Marquardt training
IEEE Transactions on Neural Networks
Semi-supervised Bayesian ARTMAP
Applied Intelligence
TopoART: a topology learning hierarchical ART network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Multilayer Fuzzy ARTMAP: fast learning and fast testing for pattern classification
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier
Engineering Applications of Artificial Intelligence
GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm
Artificial Intelligence Review
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We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fluorescence in situ hybridization image signals enabling clinical diagnosis of numerical genetic abnormalities. We evaluate the classification task (detecting a several abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy for the training patterns (averaging or voting), training mode (for one epoch, with validation or until completion) and model sensitivity to parameters. We find the fuzzy ARTMAP accurate in accomplishing both tasks requiring only very few training epochs. Also, selecting a training ordering by voting is more precise than if averaging over orderings. If trained for only one epoch, the fuzzy ARTMAP provides fast, yet stable and accurate learning as well as insensitivity to model complexity. Early stop of training using a validation set reduces the fuzzy ARTMAP complexity as for other machine learning models but cannot improve accuracy beyond that achieved when training is completed. Compared to other machine learning models, the fuzzy ARTMAP does not loose but gain accuracy when overtrained, although increasing its number of categories. Learned incrementally, the fuzzy ARTMAP reaches its ultimate accuracy very fast obtaining most of its data representation capability and accuracy by using only a few examples. Finally, the fuzzy ARTMAP accuracy for this domain is comparable with those of the multilayer perceptron and support vector machine and superior to those of the naive Bayesian and linear classifiers