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

Ibáñez Martín, Alfonso and Armañanzas Arnedillo, Ruben and Bielza Lozoya, Maria Concepcion and 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.

Description

Title: Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices
Author/s:
  • Ibáñez Martín, Alfonso
  • Armañanzas Arnedillo, Ruben
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
Item Type: Article
Título de Revista/Publicación: Journal of the Association for Information Science and Technology
Date: May 2015
ISSN: 2330-1643
Volume: 66
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2013-41592-PUnspecifiedUnspecifiedAprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering

More information

Item ID: 35438
DC Identifier: http://oa.upm.es/35438/
OAI Identifier: oai:oa.upm.es:35438
DOI: 10.1002/asi.23467
Official URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.23467
Deposited by: Memoria Investigacion
Deposited on: 12 Jun 2015 07:53
Last Modified: 30 May 2019 17:46
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