Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

Ibáñez Martín, Alfonso; Armañanzas Arnedillo, Ruben; Bielza Lozoya, Maria Concepcion y Larrañaga Múgica, Pedro (2015). Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices. "Journal of the Association for Information Science and Technology", v. 66 (n. 6); pp. 1-19. ISSN 2330-1643. https://doi.org/10.1002/asi.23467.

Descripción

Título: Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices
Autor/es:
  • Ibáñez Martín, Alfonso
  • Armañanzas Arnedillo, Ruben
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of the Association for Information Science and Technology
Fecha: Mayo 2015
Volumen: 66
Materias:
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (2MB) | Vista Previa

Resumen

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.

Más información

ID de Registro: 35438
Identificador DC: http://oa.upm.es/35438/
Identificador OAI: oai:oa.upm.es:35438
Identificador DOI: 10.1002/asi.23467
URL Oficial: http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%292330-1643
Depositado por: Memoria Investigacion
Depositado el: 12 Jun 2015 07:53
Ultima Modificación: 09 Oct 2015 10:22
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM