NDLI: Predicting the h-index with cost-sensitive naive Bayes

Content Provider	IEEE Xplore Digital Library
Author	Ibanez, A. Larranaga, P. Bielza, C.
Copyright Year	2011
Description	Author affiliation: Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain (Ibanez, A.; Larranaga, P.; Bielza, C.)
Abstract	Bibliometric indices are an increasingly important topic for the scientific community nowadays. One of the most successful bibliometric indices is the well-known h-index. In view of the attention attracted by this index, our research is based on the construction of several prediction models to forecast the h-index of Spanish professors (with a permanent position) for a four-year time horizon. We built two different types of models (junior models and senior models) to differentiate between professors' seniority. These models are learnt from bibliometric data using a cost-sensitive naive Bayes approach that takes into account the expected cost of instances predictions at classification time. Results show that it is easier to predict the h-index of the one-year time horizon than the others, that is, it has a higher average accuracy and lower average total cost than the others. Similarly, it is easier to predict the h-index of junior professors than senior professors.
Starting Page	599
Ending Page	604
File Size	241424
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121721
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	cost-sensitive naive Bayes h-index Accuracy Bibliometrics bibliometric indices Predictive models Educational institutions Data models Indexes Niobium
Content Type	Text
Resource Type	Article

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