An enhanced neural network ensemble for automatic sleep scoring

AlSukker, A; Al-Ani, A

An enhanced neural network ensemble for automatic sleep scoring

AlSukker, A Al-Ani, A

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Publication Type:: Conference Proceeding
Citation:: 2011 International Conference on Communications and Information Technology, ICCIT 2011, 2011, pp. 126 - 129
Issue Date:: 2011-05-30

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Field	Value	Language
dc.contributor.author	AlSukker, A	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.date.issued	2011-05-30	en_US
dc.identifier.citation	2011 International Conference on Communications and Information Technology, ICCIT 2011, 2011, pp. 126 - 129	en_US
dc.identifier.isbn	9781457704024	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19158
dc.description.abstract	Improving the diversity of Neural Network Ensembles (NNE) plays an important role in creating robust classification systems in many fields. Several methods have been proposed in the literature to create such diversity using different sets of classifiers or using different portions of training/feature sets. Neural networks are often used as base classifiers in multiple classifier systems as they adapt easily to small changes in the training data, therefore creating diversity that is necessary to make the ensemble work. This paper presents a novel algorithm based on generating a set of classifiers such that each one of them is biased towards one of the target classes. This will improve the ensemble diversity and hence its performance. Results on sleep data sets show that the proposed method is able to outperform the traditional fusion algorithms of bagging and boosting. © 2011 IEEE.	en_US
dc.relation.ispartof	2011 International Conference on Communications and Information Technology, ICCIT 2011	en_US
dc.relation.isbasedon	10.1109/ICCITECHNOL.2011.5762662	en_US
dc.title	An enhanced neural network ensemble for automatic sleep scoring	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Aqaba, Jordan	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Improving the diversity of Neural Network Ensembles (NNE) plays an important role in creating robust classification systems in many fields. Several methods have been proposed in the literature to create such diversity using different sets of classifiers or using different portions of training/feature sets. Neural networks are often used as base classifiers in multiple classifier systems as they adapt easily to small changes in the training data, therefore creating diversity that is necessary to make the ensemble work. This paper presents a novel algorithm based on generating a set of classifiers such that each one of them is biased towards one of the target classes. This will improve the ensemble diversity and hence its performance. Results on sleep data sets show that the proposed method is able to outperform the traditional fusion algorithms of bagging and boosting. © 2011 IEEE.

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http://hdl.handle.net/10453/19158