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Type: Artigo de periódico
Title: Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
Author: Castro, PAD
Von Zuben, FJ
Abstract: In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles.
Subject: Artificial immune system
Bayesian networks
classification problems
combinatorial optimization
ensemble of neural networks
Country: EUA
Editor: Ieee-inst Electrical Electronics Engineers Inc
Rights: fechado
Identifier DOI: 10.1109/TNN.2010.2096823
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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