Design of effective neural network ensembles for image classification purposes

In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural n...

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Bibliographic Details
Published in:Image and vision computing 2001-08, Vol.19 (9), p.699-707
Main Authors: Giacinto, Giorgio, Roli, Fabio
Format: Article
Language:English
Subjects:
Combination of classifiers
Image classification
Neural network ensembles
Remote sensing
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Summary:In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of ‘error-independent’ networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles.
ISSN:0262-8856
1872-8138
DOI:10.1016/S0262-8856(01)00045-2