Handling class imbalance problem in cultural modeling

Authors:
Peng Su;Wenji Mao;Daniel Zeng;Xiaochen Li;Fei-Yue Wang
Affiliations:
Key Laboratory of Complex Systems and Intelligence Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing and School of Management Engineering, Shandong Jianzhu University, Jinan, ...;Key Laboratory of Complex Systems and Intelligence Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China and Department of Management Information Systems, University of Ar ...;Key Laboratory of Complex Systems and Intelligence Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Year:
2009

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Abstract

Cultural modeling is an emergent and promising research area in social computing. It aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods in particular classification, play a central role in such applications. In cultural modeling, it is expected that classifiers yield good performance. However, the performance of standard classifiers is often severely hindered in practice due to the imbalanced distribution of class in cultural data. In this paper, we identify class imbalance problem in cultural modeling domain. To handle the problem, we propose a user involved solution employing the receiver operating characteristic (ROC) analysis for classification algorithms with sampling approaches. Finally, we conduct experiment to verify the effectiveness of the proposed solution.