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Title: Neural network ensembles with missing data processing and data fusion capacities: applications in medicine and in the environment
Authors: García Báez, Patricio 
Suárez Araujo, Carmen Paz 
Fernández López, Pablo 
UNESCO Clasification: 120304 Inteligencia artificial
32 Ciencias médicas
Keywords: Ensemble systems
Missing data
Data fusion
Artificial neural network
HUMANN, et al
Issue Date: 2011
Publisher: 0302-9743
Journal: Lecture Notes in Computer Science 
Conference: 11th International Work-Conference on Artificial Neural Networks (IWANN) 
11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 
Abstract: An important way to reach a qualitative improvement of Artificial Neural Networks (ANNs) is to incorporate biological features in the networks. Our proposal introduces modularity at two different levels, first, at the network level and second, at the intrinsic level of the networks, generating neural network ensembles (NNEs). We designed three NNEs which incorporated new capacities with regard to the processing of missing data, introduced hybrid modularity, and also used modular ANNs for building the NNEs. We have investigated a suitable NNE design where selection and fusion are recurrently applied to a population of best combinations of classifiers. In this paper we explore the ability of the proposed NNE in different automated decision making applications, especially for those with inherent complexity in their information environment. We present some results on dementia diagnosis and on automatic pollutants detection.
ISBN: 978-3-642-21497-4
ISSN: 0302-9743
DOI: 10.1007/978-3-642-21498-1_22
Source: Cabestany J., Rojas I., Joya G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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