Exploring the relationships between data complexity and classification diversity in ensembles

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dc.contributor.authorFormentín Garcia, Nathan
dc.contributor.authorTiggeman, Frederico
dc.contributor.authorBorges, Eduardo N.
dc.contributor.authorLucca, Giancarlo
dc.contributor.authorSantos, Helida
dc.contributor.authorPereira Dimuro, Graçaliz
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.date.accessioned2022-09-21T12:07:24Z
dc.date.available2022-09-21T12:07:24Z
dc.date.issued2021
dc.date.updated2022-09-21T11:50:49Z
dc.description.abstractSeveral classification techniques have been proposed in the last years. Each approach is best suited for a particular classification problem, i.e., a classification algorithm may not effectively or efficiently recognize some patterns in complex data. Selecting the best-tuned solution may be prohibitive. Methods for combining classifiers have also been proposed aiming at improving the generalization ability and classification results. In this paper, we analyze geometrical features of the data class distribution and the diversity of the base classifiers to understand better the performance of an ensemble approach based on stacking. The experimental evaluation was conducted using 32 real datasets, twelve data complexity measures, five diversity measures, and five heterogeneous classification algorithms. The results show that stacked generalization outperforms the best individual base classifier when there is a combination of complex and imbalanced data with diverse predictions among weak learners.en
dc.description.sponsorshipThis study was supported by CAPES Financial Code 001, PNPD/CAPES (464880/2019-00), CNPq (301618/2019-4), and FAPERGS (19/2551-0001279- 9, 19/2551-0001660).en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGarcia, N.; Tiggeman, F.; Borges, E.; Lucca, G.; Santos, H. and Dimuro, G. (2021). Exploring the Relationships between Data Complexity and Classification Diversity in Ensembles. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8; ISSN 2184-4992, pages 652-659. DOI: 10.5220/0010440006520659en
dc.identifier.doi10.5220/0010440006520659
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/44108
dc.language.isoengen
dc.publisherSciTePressen
dc.relation.ispartofFilipe, F.; Smialek, M.; Brodsky, A.; Hammoudi, S. (Eds.): Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021). Scitepress, 2021, pp. 452 - 462, 978-989-758-509-8en
dc.relation.publisherversionhttps://doi.org/10.5220/0010440006520659
dc.rights© 2021 by SCITEPRESS-Science and Technology Publications, Lda. Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectMachine learning ensemblesen
dc.subjectComplexity measuresen
dc.subjectDiversity measuresen
dc.titleExploring the relationships between data complexity and classification diversity in ensemblesen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication8c79084b-8af8-4913-a958-52ca175bd136
relation.isAuthorOfPublication4eb4bdb2-e3c9-46a2-983f-dfc0dfe20e54
relation.isAuthorOfPublication.latestForDiscovery4eb4bdb2-e3c9-46a2-983f-dfc0dfe20e54

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