caa a22 4500 445348216 CHVBK 20180317142836.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s11192-011-0366-1 doi (NATIONALLICENCE)springer-10.1007/s11192-011-0366-1 Mining typical features for highly cited papers [Elektronische Daten] [Mingyang Wang, Guang Yu, Daren Yu] In this paper, we discuss the application of the data mining tools to identify typical features for highly cited papers (HCPs). By integrating papers' external features and quality features, the feature space used to model HCPs was established. Then, a series of predictor teams were extracted from the feature space with rough set reduction framework. Each predictor team was used to construct a base classifier. Then the five base classifiers with the highest classification performance and larger diversity on whole were selected to construct a multi-classifier system (MCS) for HCPs. The combination prediction model obtained better performance than models of a single predictor team. 11 typical prediction features for HCPs were extracted on the basis of the MCS. The findings show that both the papers' inner quality and external features, mainly represented as the reputation of the authors and journals, contribute to generation of HCPs in future. Akadémiai Kiadó, Budapest, Hungary, 2011 Highly cited papers nationallicence Data mining nationallicence Citation network nationallicence Wang Mingyang School of Management, Harbin Institute of Technology, 150001, Harbin, People's Republic of China aut Yu Guang School of Management, Harbin Institute of Technology, 150001, Harbin, People's Republic of China aut Yu Daren School of Power Engineering, Harbin Institute of Technology, 150001, Harbin, People's Republic of China aut Scientometrics Springer Netherlands 87/3(2011-06-01), 695-706 0138-9130 87:3<695 2011 87 11192 https://doi.org/10.1007/s11192-011-0366-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11192-011-0366-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Mingyang School of Management, Harbin Institute of Technology, 150001, Harbin, People's Republic of China aut NATIONALLICENCE 700 1- Yu Guang School of Management, Harbin Institute of Technology, 150001, Harbin, People's Republic of China aut NATIONALLICENCE 700 1- Yu Daren School of Power Engineering, Harbin Institute of Technology, 150001, Harbin, People's Republic of China aut NATIONALLICENCE 773 0- Scientometrics Springer Netherlands 87/3(2011-06-01), 695-706 0138-9130 87:3<695 2011 87 11192 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer