A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Investigation of the Effects of Variable Vigilance within the RePART Neuro-Fuzzy Network
Journal of Intelligent and Robotic Systems
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An analysis of diversity measures
Machine Learning
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Auto-adaptive neural network tree structure based on complexity estimator
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Diversity analysis for ensembles of word sequence recognisers
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Boosting multiple classifiers constructed by hybrid discriminant analysis
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Using decision tree models and diversity measures in the selection of ensemble classification models
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
ReinSel: A class-based mechanism for feature selection in ensemble of classifiers
Applied Soft Computing
On the effect of calibration in classifier combination
Applied Intelligence
Red tides prediction system using fuzzy reasoning and the ensemble method
Applied Intelligence
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ARTMAP-based models are neural networks which use 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 models, such as ensembles or committees, since they produce robust and fast classifiers. Subsequently, some extensions of the ARTMAP model have been proposed, such as: ARTMAP-IC, RePART, among others. Aiming to add an extra contribution to ARTMAP context, this paper presents an analysis of ARTMAP-based models in ensemble systems. As a result of this analysis, two main goals are aimed, which are: to analyze the influence of the RePART model in ensemble systems and to detect any relation between diversity and accuracy in ensemble systems in order to use this relation in the design of these systems.