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Neural Computing and Applications

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01.01.2013 | Cont. Dev. of Neural Compt. & Appln.

ARTIE and MUSCLE models: building ensemble classifiers from fuzzy ART and SOM networks

verfasst von: César L. C. Mattos, Guilherme A. Barreto

Erschienen in: Neural Computing and Applications | Ausgabe 1/2013

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Abstract

Ensemble Learning has proven to be an efficient method to improve the performance of single classifiers. In this context, the present article introduces ARTIE (ART networks in Ensembles) and MUSCLE (Multiple SOM Classifiers in Ensembles), two novel ensemble models that use Fuzzy ART and SOM networks as base classifiers, respectively. In addition, a hybrid metaheuristic solution based on Particle Swarm Optimization and Simulated Annealing is used for parameter tuning of the base classifiers. A comprehensive performance comparison using 10 benchmarking data sets indicates that the ARTIE and MUSCLE architectures consistently outperform ensembles built from standard supervised neural networks, such as the Fuzzy ARTMAP, Learning Vector Quantization, and the Extreme Learning Machine.

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Titel: ARTIE and MUSCLE models: building ensemble classifiers from fuzzy ART and SOM networks
verfasst von: César L. C. Mattos
Guilherme A. Barreto
Publikationsdatum: 01.01.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0747-7

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