Creating classifier ensembles through meta-heuristic algorithms for aerial scene classification

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dc.contributor.authorFerreira, Álvaro R.
dc.contributor.authorde Rosa, Gustavo H. [UNESP]
dc.contributor.authorPapa, João P. [UNESP]
dc.contributor.authorCarneiro, Gustavo
dc.contributor.authorFaria, Fabio A.
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.contributor.institutionThe University of Adelaide
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-05-01T06:02:37Z
dc.date.available2022-05-01T06:02:37Z
dc.date.issued2020-01-01
dc.description.abstractConvolutional Neural Networks (CNN) have been being widely employed to solve the challenging remote sensing task of aerial scene classification. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the development of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios. Our results suggest that the Univariate Marginal Distribution Algorithm shows more effective and efficient results than other commonly used meta-heuristic algorithms, such as Genetic Programming and Particle Swarm Optimization.en
dc.description.affiliationInstitute of Science and Technology Universidade Federal de São Paulo
dc.description.affiliationAustralian Institute for Machine Learning The University of Adelaide
dc.description.affiliationDepartment of Computing São Paulo State University
dc.description.affiliationUnespDepartment of Computing São Paulo State University
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: #2013/07375-0
dc.description.sponsorshipIdFAPESP: #2014/12236-1
dc.description.sponsorshipIdFAPESP: #2017/25908-6
dc.description.sponsorshipIdFAPESP: #2018/23908-1
dc.description.sponsorshipIdFAPESP: #2019/02205-5
dc.description.sponsorshipIdFAPESP: #2019/07665-4
dc.format.extent415-422
dc.identifierhttp://dx.doi.org/10.1109/ICPR48806.2021.9412938
dc.identifier.citationProceedings - International Conference on Pattern Recognition, p. 415-422.
dc.identifier.doi10.1109/ICPR48806.2021.9412938
dc.identifier.issn1051-4651
dc.identifier.scopus2-s2.0-85110546434
dc.identifier.urihttp://hdl.handle.net/11449/233280
dc.language.isoeng
dc.relation.ispartofProceedings - International Conference on Pattern Recognition
dc.sourceScopus
dc.titleCreating classifier ensembles through meta-heuristic algorithms for aerial scene classificationen
dc.typeTrabalho apresentado em evento
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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