Ranking computer science conferences using self-organizing maps with dynamic node splitting

Vinicius da Silva Almendra (), Denis Enăchescu () and Cornelia Enăchescu ()
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Vinicius da Silva Almendra: University of Bucharest
Denis Enăchescu: University of Bucharest
Cornelia Enăchescu: “Gheorghe Mihoc - Caius Iacob” Institute for Mathematical Statistics and Applied Mathematics

Scientometrics, 2015, vol. 102, issue 1, No 15, 267-283

Abstract: Abstract Research dissemination in the Computer Science domain depends heavily on conference publications. The review processes of major conferences is rigorous and the work presented in those venues have more visibility and more citations than many journals, with the advantage of a faster dissemination of ideas. We consider that any evaluation system in the Computer Science domain must take into account conferences as having the same importance as journals. This makes the evaluation of venues an important issue. While journals are usually evaluated through their Impact Factor, there is no widely accepted method for evaluating conferences. In our work we analyzed the possibility of using Machine learning techniques to extend an existing ranking to new conferences, based on a set of measurements that are available for the majority of venues. Our proposal consists on the application of a Machine learning technique—self-organizing maps—with some extensions in order to classify new conferences based on an existing ranking. We also try to estimate the theoretical maximal accuracy that can be obtained using statistical learning techniques.

Keywords: Conference ranking; Machine learning; Self-organizing maps; 68P99; 62-07; 62P25 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11192-014-1436-y

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