Abstract
In this paper, the machine learning tools were used to identify key features influencing citation impact. Both the papers’ external and quality information were considered in constructing papers’ feature space. Based on the feature space, the soft fuzzy rough set was used to generate a series of associated feature subsets. Then, the KNN classifier was used to find the feature subset with the best classification performance. The results show that citation impact could be predicted by objectively assessed factors. Both the papers’ quality and external features, mainly represented as the reputation of the first author, are contributed to future citation impact.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 71003020; 70973031), the special funds of Central College Basic Scientific Research Bursary (Grant No. DL11CB09), and the Postdoctoral Science Foundation of Heilongjiang Province.
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Wang, M., Yu, G., An, S. et al. Discovery of factors influencing citation impact based on a soft fuzzy rough set model. Scientometrics 93, 635–644 (2012). https://doi.org/10.1007/s11192-012-0766-x
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DOI: https://doi.org/10.1007/s11192-012-0766-x