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Development a case-based classifier for predicting highly cited papers

Author

Listed:
  • Wang, Mingyang
  • Yu, Guang
  • Xu, Jianzhong
  • He, Huixin
  • Yu, Daren
  • An, Shuang

Abstract

In this paper, we discussed the feasibility of early recognition of highly cited papers with citation prediction tools. Because there are some noises in papers’ citation behaviors, the soft fuzzy rough set (SFRS), which is well robust to noises, is introduced in constructing the case-based classifier (CBC) for highly cited papers. After careful design that included: (a) feature reduction by SFRS; (b) case selection by the combination use of SFRS and the concept of case coverage; (c) reasoning by two classification techniques of case coverage based prediction and case score based prediction, this study demonstrates that the highly cited papers could be predicted by objectively assessed factors. It shows that features included the research capabilities of the first author, the papers’ quality and the reputation of journal are the most relevant predictors for highly cited papers.

Suggested Citation

  • Wang, Mingyang & Yu, Guang & Xu, Jianzhong & He, Huixin & Yu, Daren & An, Shuang, 2012. "Development a case-based classifier for predicting highly cited papers," Journal of Informetrics, Elsevier, vol. 6(4), pages 586-599.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:4:p:586-599
    DOI: 10.1016/j.joi.2012.06.002
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    References listed on IDEAS

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    Cited by:

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    3. Hu, Ya-Han & Tai, Chun-Tien & Liu, Kang Ernest & Cai, Cheng-Fang, 2020. "Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity," Journal of Informetrics, Elsevier, vol. 14(1).

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