Repository logo
 
Publication

Choice of Best Samples for Building Ensembles in Dynamic Environments

2016Book part Restricted access
Simple item page
dc.contributor.authorCosta, Joana
dc.contributor.authorSilva, Catarina
dc.contributor.authorAntunes, Mário
dc.contributor.authorRibeiro, Bernardete
dc.date.accessioned2025-07-17T13:46:17Z
dc.date.available2025-07-17T13:46:17Z
dc.date.issued2016
dc.description.abstractMachine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.eng
dc.description.sponsorshipThis work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project “POCI-01-0145-FEDER-006961”, and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. This work was supported by national funds through the Portuguese Foundation for Science and Technology (FCT), and by the European Regional Development Fund (FEDER) through COMPETE 2020 – Operational Program for Competitiveness and Internationalization (POCI).
dc.identifier.citationCosta, J., Silva, C., Antunes, M., Ribeiro, B. (2016). Choice of Best Samples for Building Ensembles in Dynamic Environments. In: Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-44188-7_3
dc.identifier.doi10.1007/978-3-319-44188-7_3
dc.identifier.isbn9783319441870
dc.identifier.isbn9783319441887
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10400.8/13700
dc.language.isoeng
dc.peerreviewedn/a
dc.publisherSpringer International Publishing
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-319-44188-7_3
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofEngineering Applications of Neural Networks
dc.rights.uriN/A
dc.subjectDynamic Environments
dc.subjectEnsembles
dc.subjectDrift
dc.subjectText Classification
dc.subjectSocial Networks
dc.titleChoice of Best Samples for Building Ensembles in Dynamic Environmentseng
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage47
oaire.citation.startPage35
oaire.citation.titleEngineering Applications of Neural Networks (EANN 2016)
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCosta
person.familyNameSilva
person.familyNameAntunes
person.givenNameJoana
person.givenNameCatarina
person.givenNameMário
person.identifierR-000-NX4
person.identifier.ciencia-idAF10-7EDD-5153
person.identifier.orcid0000-0002-4053-5718
person.identifier.orcid0000-0002-5656-0061
person.identifier.orcid0000-0003-3448-6726
person.identifier.scopus-author-id25930820200
relation.isAuthorOfPublication23d200dc-1a81-4bd9-9a4a-0efc28af6ce4
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublicatione3e87fb0-d1d6-44c3-985d-920a5560f8c1
relation.isAuthorOfPublication.latestForDiscovery23d200dc-1a81-4bd9-9a4a-0efc28af6ce4

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
318.pdf
Size:
514.44 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.32 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESTG - Comunicações em conferências e congressos internacionais