Surrogate-assisted and filter-based multi-objective evolutionary feature selection for deep learning

Espinosa Fernández, Raquel; Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás

Publication:
Surrogate-assisted and filter-based multi-objective evolutionary feature selection for deep learning

dc.contributor.author	Espinosa Fernández, Raquel
dc.contributor.author	Jiménez Barrionuevo, Fernando
dc.contributor.author	Palma Méndez, José Tomás
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2023-01-16T08:25:08Z
dc.date.available	2023-01-16T08:25:08Z
dc.date.issued	2023-01-12
dc.description	© 2023, Publishers. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in IEEE Transactions on Neural Networks and Learning Systems. To access the final edited and published work see https://doi.org/10.1109/TNNLS.2023.3234629	es
dc.description.abstract	Feature selection for deep learning prediction mod- els is a difficult topic for researchers to tackle. Most of the ap- proaches proposed in the literature consist of embedded methods through the use of hidden layers added to the neural network architecture that modify the weights of the units associated with each input attribute so that the worst attributes have less weight in the learning process. Other approaches used for deep learning are filter methods, which are independent of the learning algorithm, which can limit the precision of the prediction model. Wrapper methods are impractical with deep learning due to their high computational cost. In this paper, we propose new attribute subset evaluation feature selection methods for deep learning of the wrapper, filter and wrapper-filter hybrid types, where multi-objective and many-objective evolutionary algorithms are used as search strategies. A novel surrogate-assisted approach is used to reduce the high computational cost of the wrapper-type objective function, while the filter-type objective functions are based on correlation and an adaptation of the reliefF algorithm. The proposed techniques have been applied in a time series forecasting problem of air quality in the Spanish south-east and an indoor temperature forecasting problem in a domotic house, with promising results compared to other feature se	es
dc.format	application/pdf	es
dc.format.extent	15	es
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems
dc.identifier.doi	https://doi.org/10.1109/TNNLS.2023.3234629
dc.identifier.issn	2162-237X
dc.identifier.issn	2162-2388
dc.identifier.uri	http://hdl.handle.net/10201/127323
dc.language	eng	es
dc.publisher	Institute of Electrical and Electronics Engineers	es
dc.relation	This work was partially funded by the CONFAINCE project (Ref: PID2021-122194OB-I00), supported by the Spanish Ministry of Science and Innovation and the Spanish Agency for Research, and the IMPACT-T2D project (PMP21/00092) supported by the Spanish Health Institute Carlos III (ISCIII).	es
dc.relation.isreplacedby	10.1109/TNNLS.2023.3234629	es
dc.relation.publisherversion	https://ieeexplore.ieee.org/document/10016286	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Feature selection	es
dc.subject	deep learning	es
dc.subject	surrogate- assisted	es
dc.subject	multi-objective evolutionary algorithms	es
dc.subject	time series forecasting	es
dc.subject	air quality	es
dc.subject	indoor temperature	es
dc.subject.other	CDU::6 - Ciencias aplicadas::68 - Industrias, oficios y comercio de artículos acabados. Tecnología cibernética y automática	es
dc.title	Surrogate-assisted and filter-based multi-objective evolutionary feature selection for deep learning	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es

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