Development a case-based classifier for predicting highly cited papers |
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| Authors: | Mingyang Wang Guang Yu Jianzhong Xu Huixin He Daren Yu Shuang An |
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| Institution: | 1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, PR China;2. School of Management, Harbin Institute of Technology, 150001, PR China;3. School of Economics and Management, Harbin Engineering University, 150001, PR China;4. School of Power Engineering, Harbin Institute of Technology, 150001, PR China;5. School of Information and Computer Science, Northeastern University at Qinhuangdao, 066004, PR China;1. REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal;2. Departamento Engenharia Industrial e Gestão, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal;3. INESC-TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal;1. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong;2. Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd., Singapore;1. University of Barcelona, Barcelona, Spain;2. IESEG School of Management (LEM CNRS 9221), Lille/Paris, France |
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| 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. |
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