Repositorio Institucional
Repositorio Institucional
CONICET Digital
  • Inicio
  • EXPLORAR
    • AUTORES
    • DISCIPLINAS
    • COMUNIDADES
  • Estadísticas
  • Novedades
    • Noticias
    • Boletines
  • Ayuda
    • General
    • Datos de investigación
  • Acerca de
    • CONICET Digital
    • Equipo
    • Red Federal
  • Contacto
JavaScript is disabled for your browser. Some features of this site may not work without it.
  • INFORMACIÓN GENERAL
  • RESUMEN
  • ESTADISTICAS
 
Artículo

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

Zhang, Jin; Feng, Fan; Han, TianYi; Duan, Feng; Sun, Zhe; Caiafa, César FedericoIcon ; Solé Casals, Jordi
Fecha de publicación: 08/2021
Editorial: Springer
Revista: Science China Technological Sciences
ISSN: 1674-7321
e-ISSN: 1869-1900
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Información y Bioinformática

Resumen

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.
Palabras clave: Tensor completion , brain sciences , gifted children
Ver el registro completo
 
Archivos asociados
Thumbnail
 
Tamaño: 595.8Kb
Formato: PDF
.
Descargar
Licencia
info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/146028
URL: https://link.springer.com/10.1007/s11431-020-1876-3
DOI: http://dx.doi.org/10.1007/s11431-020-1876-3
Colecciones
Articulos(IAR)
Articulos de INST.ARG.DE RADIOASTRONOMIA (I)
Citación
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
Compartir
Altmétricas
 

Enviar por e-mail
Separar cada destinatario (hasta 5) con punto y coma.
  • Facebook
  • X Conicet Digital
  • Instagram
  • YouTube
  • Sound Cloud
  • LinkedIn

Los contenidos del CONICET están licenciados bajo Creative Commons Reconocimiento 2.5 Argentina License

https://www.conicet.gov.ar/ - CONICET

Inicio

Explorar

  • Autores
  • Disciplinas
  • Comunidades

Estadísticas

Novedades

  • Noticias
  • Boletines

Ayuda

Acerca de

  • CONICET Digital
  • Equipo
  • Red Federal

Contacto

Godoy Cruz 2290 (C1425FQB) CABA – República Argentina – Tel: +5411 4899-5400 repositorio@conicet.gov.ar
TÉRMINOS Y CONDICIONES