C4.5: programs for machine learning
C4.5: programs for machine learning
How can we investigate citation behavior?: a study of reasons for citing literature in communication
Journal of the American Society for Information Science
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Criminal network analysis and visualization
Communications of the ACM - 3d hard copy
Analysis of terrorist social networks with fractal views
Journal of Information Science
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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.