Using ARTMAP-Based Ensemble Systems Designed by Three Variants of Boosting

Authors:
Araken Medeiros Santos;Anne Magaly Paula Canuto
Affiliations:
Informatics and Applied Mathematics Department, Federal University of RN Natal, Brazil 59072-970;Informatics and Applied Mathematics Department, Federal University of RN Natal, Brazil 59072-970
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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Abstract

This paper analyzes the use of ARTMAP-based in structures of ensembles designed by three variants of boosting (Aggressive, Conservative and Inverse). In this investigation, it is aimed to analyze the influence of the RePART (Reward and Punishment ARTmap) neural network in ARTMAP-based ensembles, intending to define whether the use of this model is positive for ARTMAP-based ensembles. In addition, it aims to define which boosting strategy is the most suitable to be used in ARTMAP-based ensembles.