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Discovery of factors influencing citation impact based on a soft fuzzy rough set model

机译：基于软模糊粗糙集模型的影响引文影响因素的发现

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摘要

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.
机译：在本文中，机器学习工具用于识别影响引文影响的关键特征。在构建文件的特征空间时，会同时考虑文件的外部信息和质量信息。基于特征空间，使用软模糊粗糙集生成一系列关联的特征子集。然后，使用KNN分类器找到具有最佳分类性能的特征子集。结果表明，可以通过客观评估因素来预测引用影响。论文的质量和外部特征（主要表现为第一作者的声誉）都对未来的引文影响有所贡献。

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  • 来源
    《Scientometrics》 |2012年第3期|p.635-644|共10页
  • 作者

    Mingyang Wang; Guang Yu; Shuang An; Daren Yu;

  • 作者单位

    College of Information and Computer Engineering, Northeast Forestry University, Harbin, 150040, People’s Republic of China;

    School of Management, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China;

    School of Power Engineering, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China;

    School of Power Engineering, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Citation impact; Highly cited papers; Soft fuzzy rough set;

    机译：引文影响;高被引论文;软模糊粗糙集;

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