Relevance of Innovations in Machine Learning to Scientometrics
Gowri Srinivasa1★★ Corresponding author
- 1Center for Pattern Recognition and Department of Computer Science and Engineering, PES University, EC Campus, Bengaluru, Karnataka, INDIA.
CORRESPONDENCE
Gowri Srinivasa
Center for Pattern Recognition and Department of Computer Science and Engineering, PES University, EC Campus, Bengaluru, Karnataka, INDIA.
Received: 16-02-2019; Revised: 30-04-2019; Accepted: 11-06-2019.
Volume 8, Issue 2S · pp. s39–s43 · PUBLISHED · DOI: 10.5530/jscires.8.2.23
ABSTRACT
Machine learning envisages building models that either classify, predict, cluster or determine the relative relevance of features to a problem and the associations between them. This paper briefy describes how these tasks are relevant to Scientometrics. Through this brief survey of selected tasks, it is observed that most solution approaches in Scientometric literature are built on the strong foundation of understanding and debating in uencing factors and the process of feature engineering, requiring the descriptors to be intuitive and methods used for classication, prediction, etc., to be amenable to interpretation. Recent trends in machine learning, particularly, deep learning methods, however, pose an interesting question: can we build models that automatically determine what features are important and thereby bypass the step of feature engineering? This paper discusses how such techniques could also be harnessed in Scientometrics.
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Srinivasa, G. (). Relevance of Innovations in Machine Learning to Scientometrics. Journal of Scientometric Research, 8(2S), s39–s43. https://doi.org/10.5530/jscires.8.2.23