Ensemble learning via feature selection and multiple transformed subsets: Application to image classification

[EN]In the machine learning field, especially in classification tasks, the model's design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can pr...

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Detalles Bibliográficos
Autores: Khoder, Ahmad, Dornaika, Fadi
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/54483
Acceso en línea:http://hdl.handle.net/10810/54483
Access Level:acceso abierto
Palabra clave:ensemble learning
feature subsets
multi-models
machine learning
feature selection
image classification
class sparsity least square regression
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spelling Ensemble learning via feature selection and multiple transformed subsets: Application to image classificationKhoder, AhmadDornaika, Fadiensemble learningfeature subsetsmulti-modelsmachine learningfeature selectionimage classificationclass sparsity least square regression[EN]In the machine learning field, especially in classification tasks, the model's design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can provide. To this end, Ensemble learning methods were proposed in the literature. These methods' main goal is to learn a set of models that provide features or predictions whose joint use could lead to a performance better than that obtained by the single model. In this paper, we propose three variants of a new efficient ensemble learning approach that was able to enhance the classification performance of a linear discriminant embedding method. As a case study we consider the efficient "Inter-class sparsity discriminative least square regression" method. We seek the estimation of an enhanced data representation. Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings. These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations. The derived extracted feature subsets were concatenated to form a single data representation vector that is used in the classification process. Many factors were studied and investigated in this paper including (Parameter combinations, number of models, different training percentages, feature selection methods combinations, etc.). Our proposed approach has been benchmarked on different image datasets of various sizes and types (faces, objects and scenes). The proposed scheme achieved competitive performance on four face image datasets (Extended Yale B, LFW-a, Gorgia and FEI) as well as on the COIL20 object dataset and the Outdoor Scene dataset. We measured the performance of our proposed schemes in comparison to (the single model ICS_DLSR, RDA_GD, RSLDA, PCE, LDE, LDA, SVM as well as the KNN algorithm) The conducted experiments showed that the proposed approach can enhance the classification performance in an efficient manner compared to the single-model based learning and was able to outperform its competing methods.Elsevier202120212021info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/54483reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.sciencedirect.com/science/article/pii/S1568494621009285?via%3Dihubinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/3.0/es/©2021 The Author(s). This is an open access article under the CC BY-NC-ND licenseAtribución-NoComercial-SinDerivadas 3.0 Españaoai:addi.ehu.eus:10810/544832026-06-18T09:23:17Z
dc.title.none.fl_str_mv Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
title Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
spellingShingle Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
Khoder, Ahmad
ensemble learning
feature subsets
multi-models
machine learning
feature selection
image classification
class sparsity least square regression
title_short Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
title_full Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
title_fullStr Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
title_full_unstemmed Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
title_sort Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
dc.creator.none.fl_str_mv Khoder, Ahmad
Dornaika, Fadi
author Khoder, Ahmad
author_facet Khoder, Ahmad
Dornaika, Fadi
author_role author
author2 Dornaika, Fadi
author2_role author
dc.subject.none.fl_str_mv ensemble learning
feature subsets
multi-models
machine learning
feature selection
image classification
class sparsity least square regression
topic ensemble learning
feature subsets
multi-models
machine learning
feature selection
image classification
class sparsity least square regression
description [EN]In the machine learning field, especially in classification tasks, the model's design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can provide. To this end, Ensemble learning methods were proposed in the literature. These methods' main goal is to learn a set of models that provide features or predictions whose joint use could lead to a performance better than that obtained by the single model. In this paper, we propose three variants of a new efficient ensemble learning approach that was able to enhance the classification performance of a linear discriminant embedding method. As a case study we consider the efficient "Inter-class sparsity discriminative least square regression" method. We seek the estimation of an enhanced data representation. Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings. These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations. The derived extracted feature subsets were concatenated to form a single data representation vector that is used in the classification process. Many factors were studied and investigated in this paper including (Parameter combinations, number of models, different training percentages, feature selection methods combinations, etc.). Our proposed approach has been benchmarked on different image datasets of various sizes and types (faces, objects and scenes). The proposed scheme achieved competitive performance on four face image datasets (Extended Yale B, LFW-a, Gorgia and FEI) as well as on the COIL20 object dataset and the Outdoor Scene dataset. We measured the performance of our proposed schemes in comparison to (the single model ICS_DLSR, RDA_GD, RSLDA, PCE, LDE, LDA, SVM as well as the KNN algorithm) The conducted experiments showed that the proposed approach can enhance the classification performance in an efficient manner compared to the single-model based learning and was able to outperform its competing methods.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/54483
url http://hdl.handle.net/10810/54483
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S1568494621009285?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
©2021 The Author(s). This is an open access article under the CC BY-NC-ND license
Atribución-NoComercial-SinDerivadas 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/3.0/es/
©2021 The Author(s). This is an open access article under the CC BY-NC-ND license
Atribución-NoComercial-SinDerivadas 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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