Optimum-Path Forest Ensembles to Estimate the Internal Decay in Urban Trees

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorCandido, Giovani-
Autor(es): dc.creatorMorelli, Luis Henrique-
Autor(es): dc.creatorJodas, Danilo Samuel-
Autor(es): dc.creatorVelasco, Giuliana Del Nero-
Autor(es): dc.creatorde Lima, Reinaldo Araújo-
Autor(es): dc.creatorda Costa, Kelton Augusto Pontara-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T16:23:23Z-
Data de disponibilização: dc.date.available2025-08-21T16:23:23Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0013113600003912-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305484-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305484-
Descrição: dc.descriptionResearch on urban tree management has recently grown to include various studies using machine learning to address the tree’s risk of falling. One significant challenge is to assess the extent of internal decay, a crucial factor contributing to tree breakage. This paper uses machine and ensemble learning algorithms to determine internal trunk decay levels. Notably, it introduces a novel variation of the Optimum-Path Forest (OPF) ensemble pruning method, OPFsemble, which incorporates a “count class” strategy and performs weighted majority voting for ensemble predictions. To optimize the models’ hyperparameters, we employ a slime mold-inspired metaheuristic, and the optimized models are then applied to the classification task. The optimized hyperparameters are used to randomly select distinct configurations for each model across ensemble techniques such as voting, stacking, and OPFsemble. Our OPFsemble variant is compared to the original one, which serves as a baseline. Moreover, the estimated levels of internal decay are used to predict the tree’s risk of falling and evaluate the proposed approach’s reliability. Experimental results demonstrate the effectiveness of the proposed method in determining internal trunk decay. Furthermore, the findings reveal the potential of the proposed ensemble pruning in reducing ensemble models while attaining competitive performance.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences-
Descrição: dc.descriptionInstitute for Technological Research University of São Paulo-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences-
Descrição: dc.descriptionFAPESP: #2022/16562-7-
Descrição: dc.descriptionFAPESP: #2023/12830-0-
Formato: dc.format895-902-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectEnsemble Learning-
Palavras-chave: dc.subjectInternal Trunk Decay-
Palavras-chave: dc.subjectMachine Learning-
Palavras-chave: dc.subjectMetaheuristics-
Palavras-chave: dc.subjectUrban Tree Risk Management-
Título: dc.titleOptimum-Path Forest Ensembles to Estimate the Internal Decay in Urban Trees-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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