Feature Selection and Hyperparameter Fine-Tuning in Artificial Neural Networks for Wood Quality Classification

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorPaulo State University-
Autor(es): dc.creatorRoder, Mateus-
Autor(es): dc.creatorPassos, Leandro Aparecido-
Autor(es): dc.creatorPapa, João Paulo-
Autor(es): dc.creatorRossi, André Luis Debiaso-
Data de aceite: dc.date.accessioned2025-08-21T19:10:34Z-
Data de disponibilização: dc.date.available2025-08-21T19:10:34Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-45389-2_22-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306030-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306030-
Descrição: dc.descriptionQuality classification of wood boards is an essential task in the sawmill industry, which is still usually performed by human operators in small to median companies in developing countries. Machine learning algorithms have been successfully employed to investigate the problem, offering a more affordable alternative compared to other solutions. However, such approaches usually present some drawbacks regarding the proper selection of their hyperparameters. Moreover, the models are susceptible to the features extracted from wood board images, which influence the induction of the model and, consequently, its generalization power. Therefore, in this paper, we investigate the problem of simultaneously tuning the hyperparameters of an artificial neural network (ANN) as well as selecting a subset of characteristics that better describes the wood board quality. Experiments were conducted over a private dataset composed of images obtained from a sawmill industry and described using different feature descriptors. The predictive performance of the model was compared against five baseline methods as well as a random search, performing either ANN hyperparameter tuning and feature selection. Experimental results suggest that hyperparameters should be adjusted according to the feature set, or the features should be selected considering the hyperparameter values. In summary, the best predictive performance, i.e., a balanced accuracy of 0.80, was achieved in two distinct scenarios: (i) performing only feature selection, and (ii) performing both tasks concomitantly. Thus, we suggest that at least one of the two approaches should be considered in the context of industrial applications.-
Descrição: dc.descriptionDepartment of Computing São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Descrição: dc.descriptionDepartment of Production Engineering Paulo State University, Rua Geraldo Alckmin, 519 - Vila Nossa Sra. de Fatima-
Descrição: dc.descriptionDepartment of Computing São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01-
Formato: dc.format323-337-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
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Palavras-chave: dc.subjectArtificial Neural Network Optimization-
Palavras-chave: dc.subjectFeature Selection-
Palavras-chave: dc.subjectHyperparameter Tuning-
Palavras-chave: dc.subjectSawmill Problem-
Palavras-chave: dc.subjectWood Quality Classification-
Título: dc.titleFeature Selection and Hyperparameter Fine-Tuning in Artificial Neural Networks for Wood Quality Classification-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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