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Neural Computing and Applications

Erschienen in:

01.06.2012 | Original Article

A new artificial neural network ensemble based on feature selection and class recoding

verfasst von: M. P. Sesmero, J. M. Alonso-Weber, G. Gutiérrez, A. Ledezma, A. Sanchis

Erschienen in: Neural Computing and Applications | Ausgabe 4/2012

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Abstract

Many of the studies related to supervised learning have focused on the resolution of multiclass problems. A standard technique used to resolve these problems is to decompose the original multiclass problem into multiple binary problems. In this paper, we propose a new learning model applicable to multi-class domains in which the examples are described by a large number of features. The proposed model is an Artificial Neural Network ensemble in which the base learners are composed by the union of a binary classifier and a multiclass classifier. To analyze the viability and quality of this system, it will be validated in two real domains: traffic sign recognition and hand-written digit recognition. Experimental results show that our model is at least as accurate as other methods reported in the bibliography but has a considerable advantage respecting size, computational complexity, and running time.

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Titel: A new artificial neural network ensemble based on feature selection and class recoding
verfasst von: M. P. Sesmero
J. M. Alonso-Weber
G. Gutiérrez
A. Ledezma
A. Sanchis
Publikationsdatum: 01.06.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0458-5

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