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Genetic algorithms and G aussian B ayesian networks to uncover the predictive core set of bibliometric indices
Author(s) -
Ibáñez Alfonso,
Armañanzas Rubén,
Bielza Concha,
Larrañaga Pedro
Publication year - 2016
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23467
Subject(s) - multivariate statistics , index (typography) , set (abstract data type) , variable (mathematics) , core (optical fiber) , computer science , data set , data mining , bibliometrics , mathematics , machine learning , artificial intelligence , mathematical analysis , telecommunications , world wide web , programming language
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 G aussian B ayesian 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 G aussian B ayesian 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, q 2 ‐index, and h r ‐index. This research is performed using bibliometric data on S panish full professors associated with the computer science area.