Evaluation of Machine Learning to Early Detection of Highly Cited Papers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

As one of the fastest-growing topics, machine learning has many applications that span through different domains including image and signal recognition, text mining, information retrieval, robotics, etc. It enables information extraction and analysis for better insights and decision-based systems. The Web of Science(WoS) citation database is a leading organization that provides citation data of high-quality published research. WoS has its metrics to label published articles as Highly Cited Paper(HCP). Machine learning (ML) can help researchers in identifying the key characteristics of HCP. Moreover, it can allow research evaluation units forecasting significant scientific articles. In other words, it may allow researchers and/or research evaluators to detect potential scientific breakthrough ideas and stay current. In this study, more than 26 thousand records of published articles indexed by WoS were analyzed. All the records are drawn from the Technology research area as defined by WoS. Four ML algorithms are evaluated to verify the HCP common factors influence in raising citations and interest in scientific articles. The ensemble algorithms show promising results to identify HCP articles using only four factors.

Original languageEnglish
Title of host publicationProceedings - 2022 7th International Conference on Data Science and Machine Learning Applications, CDMA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781665410144
DOIs
StatePublished - 2022

Publication series

NameProceedings - 2022 7th International Conference on Data Science and Machine Learning Applications, CDMA 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Bibliometric Analysis
  • Digital Libraries
  • Highly-cited Research
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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