A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

Autores
Zhang, Jin; Feng, Fan; Han, TianYi; Duan, Feng; Sun, Zhe; Caiafa, César Federico; Solé Casals, Jordi
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.
Fil: Zhang, Jin. Nankai University; China
Fil: Feng, Fan. Nankai University; China
Fil: Han, TianYi. Nankai University; China
Fil: Duan, Feng. Nankai University; China
Fil: Sun, Zhe. Riken. Brain Science Institute; Japón
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Solé Casals, Jordi. Central University of Catalonia; España
Materia
Tensor completion
brain sciences
gifted children
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/146028
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identificationZhang, JinFeng, FanHan, TianYiDuan, FengSun, ZheCaiafa, César FedericoSolé Casals, JordiTensor completionbrain sciencesgifted childrenhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.Fil: Zhang, Jin. Nankai University; ChinaFil: Feng, Fan. Nankai University; ChinaFil: Han, TianYi. Nankai University; ChinaFil: Duan, Feng. Nankai University; ChinaFil: Sun, Zhe. Riken. Brain Science Institute; JapónFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Solé Casals, Jordi. Central University of Catalonia; EspañaSpringer2021-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/146028Zhang, Jin; Feng, Fan; Han, TianYi; Duan, Feng; Sun, Zhe; et al.; A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification; Springer; Science China Technological Sciences; 64; 8-2021; 1863–18711674-73211869-1900CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s11431-020-1876-3info:eu-repo/semantics/altIdentifier/doi/10.1007/s11431-020-1876-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:44:01Zoai:ri.conicet.gov.ar:11336/146028instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:44:01.507CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
title A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
spellingShingle A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
Zhang, Jin
Tensor completion
brain sciences
gifted children
title_short A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
title_full A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
title_fullStr A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
title_full_unstemmed A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
title_sort A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification
dc.creator.none.fl_str_mv Zhang, Jin
Feng, Fan
Han, TianYi
Duan, Feng
Sun, Zhe
Caiafa, César Federico
Solé Casals, Jordi
author Zhang, Jin
author_facet Zhang, Jin
Feng, Fan
Han, TianYi
Duan, Feng
Sun, Zhe
Caiafa, César Federico
Solé Casals, Jordi
author_role author
author2 Feng, Fan
Han, TianYi
Duan, Feng
Sun, Zhe
Caiafa, César Federico
Solé Casals, Jordi
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Tensor completion
brain sciences
gifted children
topic Tensor completion
brain sciences
gifted children
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.
Fil: Zhang, Jin. Nankai University; China
Fil: Feng, Fan. Nankai University; China
Fil: Han, TianYi. Nankai University; China
Fil: Duan, Feng. Nankai University; China
Fil: Sun, Zhe. Riken. Brain Science Institute; Japón
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Solé Casals, Jordi. Central University of Catalonia; España
description Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.
publishDate 2021
dc.date.none.fl_str_mv 2021-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/146028
Zhang, Jin; Feng, Fan; Han, TianYi; Duan, Feng; Sun, Zhe; et al.; A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification; Springer; Science China Technological Sciences; 64; 8-2021; 1863–1871
1674-7321
1869-1900
CONICET Digital
CONICET
url http://hdl.handle.net/11336/146028
identifier_str_mv Zhang, Jin; Feng, Fan; Han, TianYi; Duan, Feng; Sun, Zhe; et al.; A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification; Springer; Science China Technological Sciences; 64; 8-2021; 1863–1871
1674-7321
1869-1900
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s11431-020-1876-3
info:eu-repo/semantics/altIdentifier/doi/10.1007/s11431-020-1876-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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