IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v6y2012i4p586-599.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157712000454
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2012.06.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bornmann, Lutz & Leydesdorff, Loet, 2012. "Which are the best performing regions in information science in terms of highly cited papers? Some improvements of our previous mapping approaches," Journal of Informetrics, Elsevier, vol. 6(2), pages 336-345.
    2. Quentin L. Burrell, 2003. "Predicting future citation behavior," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(5), pages 372-378, March.
    3. Hendrik P. van Dalen & K?ne Henkens, 2005. "Signals in science - On the importance of signaling in gaining attention in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(2), pages 209-233, August.
    4. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    5. Mingyang Wang & Guang Yu & Daren Yu, 2011. "Mining typical features for highly cited papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 695-706, June.
    6. Laband, David N & Piette, Michael J, 1994. "Favoritism versus Search for Good Papers: Empirical Evidence Regarding the Behavior of Journal Editors," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 194-203, February.
    7. W Glänzel & E J Rinia & M G M Brocken, 1995. "A bibliometric study of highly cited European physics papers in the 80s," Research Evaluation, Oxford University Press, vol. 5(2), pages 113-122, August.
    8. Lawrence D. Fu & Constantin F. Aliferis, 2010. "Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 257-270, October.
    9. Jonathan M. Levitt & Mike Thelwall, 2008. "Is multidisciplinary research more highly cited? A macrolevel study," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(12), pages 1973-1984, October.
    10. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    11. Quentin L. Burrell, 2002. "Will this paper ever be cited?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(3), pages 232-235.
    12. Hendrik P. Van Dalen & Kène Henkens, 2001. "What makes a scientific article influential? The case of demographers," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 455-482, March.
    13. Moed, Henk F., 2010. "Measuring contextual citation impact of scientific journals," Journal of Informetrics, Elsevier, vol. 4(3), pages 265-277.
    14. Donald O. Case & Georgeann M. Higgins, 2000. "How can we investigate citation behavior? A study of reasons for citing literature in communication," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(7), pages 635-645.
    15. Quentin L. Burrell, 2002. "The nth-citation distribution and obsolescence," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(3), pages 309-323, March.
    16. Dag W Aksnes, 2003. "Characteristics of highly cited papers," Research Evaluation, Oxford University Press, vol. 12(3), pages 159-170, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    2. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mingyang Wang & Guang Yu & Shuang An & Daren Yu, 2012. "Discovery of factors influencing citation impact based on a soft fuzzy rough set model," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 635-644, December.
    2. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    3. Mingyang Wang & Shi Li & Guangsheng Chen, 2017. "Detecting latent referential articles based on their vitality performance in the latest 2 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1557-1571, September.
    4. Mingyang Wang & Guang Yu & Daren Yu, 2011. "Mining typical features for highly cited papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 695-706, June.
    5. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    6. Vahid Garousi & João M. Fernandes, 2017. "Quantity versus impact of software engineering papers: a quantitative study," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 963-1006, August.
    7. Peter Klimek & Aleksandar Jovanovic & Rainer Egloff & Reto Schneider, 2016. "Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1265-1282, June.
    8. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    9. Basma Albanna & Julia Handl & Richard Heeks, 2021. "Publication outperformance among global South researchers: An analysis of individual-level and publication-level predictors of positive deviance," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8375-8431, October.
    10. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    11. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    12. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
    13. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
    14. Yang, Jinqing & Liu, Zhifeng, 2022. "The effect of citation behaviour on knowledge diffusion and intellectual structure," Journal of Informetrics, Elsevier, vol. 16(1).
    15. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.
    16. Elizabeth S. Vieira, 2023. "The influence of research collaboration on citation impact: the countries in the European Innovation Scoreboard," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3555-3579, June.
    17. Petr Heneberg, 2013. "Lifting the fog of scientometric research artifacts: On the scientometric analysis of environmental tobacco smoke research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 334-344, February.
    18. Burrell, Quentin L., 2013. "A stochastic approach to the relation between the impact factor and the uncitedness factor," Journal of Informetrics, Elsevier, vol. 7(3), pages 676-682.
    19. Burrell, Quentin L., 2007. "Hirsch's h-index: A stochastic model," Journal of Informetrics, Elsevier, vol. 1(1), pages 16-25.
    20. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:6:y:2012:i:4:p:586-599. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.