A Compact and Accurate Model for Classification
IEEE Transactions on Knowledge and Data Engineering
Empirical Evaluation of Optimized Stacking Configurations
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
An inclusion/exclusion fuzzy hyperbox classifier
International Journal of Knowledge-based and Intelligent Engineering Systems - Advanced Intelligent Techniques in Engineering Applications
An empirical risk functional to improve learning in a neuro-fuzzy classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Adaptive resolution min-max classifiers
IEEE Transactions on Neural Networks
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This paper presents a neural network classifier based on fuzzy ARTMAP with conflict-resolving strategy. The proposed model explicitly resolves overlaps among prototypes of different classes through deploying a contraction procedure in the network, therefore, improving its generalization. Compared with other existing methods, the model has the priority of intuition and no parameter tuning. The performance of the model is evaluated using several benchmark data sets. The comparisons with original fuzzy ARTMAP model and other classifiers indicate that the proposed classifier achieves competitive performance.