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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
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
||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
Larrañaga Múgica, Pedro
|Título de Revista/Publicación:
||Journal of the Association for Information Science and Technology
||E.T.S. de Ingenieros Informáticos (UPM)
|Creative Commons Licenses:
||Recognition - No derivative works - Non commercial
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
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