›› 2009, Vol. 24 ›› Issue (6): 1010-1017.

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section on International Partnership Programs Supported by CAS • Previous Articles     Next Articles

Performance Evaluation of Machine Learning Methods in Cultural Modeling

Xiao-Chen Li1 (李晓晨), Wen-Ji Mao1 (毛文吉), Senior Member, CCF, Member, ACM, Daniel Zeng1, 2 (曾大军), Senior Member, IEEE, Peng Su1, 3 (苏鹏), and Fei-Yue Wang1 (王飞跃), Senior Member CCF, Fellow, IEEE   

  1. 1Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Scienceshspace*3mm}Beijing 100190, China
    2Department of Management Information Systems, University of Arizona, Tucson AZ 85721, U.S.A.
    3School of Management Engineering, Shandong Jianzhu University, Jinan 250013, China
  • Received:2009-03-08 Revised:2009-07-20 Online:2009-11-05 Published:2009-11-05
  • About author:
    Xiao-Chen Li is a Ph.D. candidate at the Institute of Automation, Chinese Academy of Sciences. His research interests mainly focus on social computing and agent-based modeling.
    Wen-Ji Mao received her Ph.D. degree in computer science from the University of Southern California in 2006. She is an associate professor at the Institute of Automation, Chinese Academy of Sciences. Prof. Mao is a member of ACM and AAAI, and a senior member of the China Computer Federation. Her research interests include artificial intelligence, multi-agent systems and social modeling.
    Daniel Zeng received his Ph.D. degree in industrial administration from Carnegie Mellon University in 1998. He is a research professor at the Institute of Automation, Chinese Academy of Sciences. He is also affiliated with the University of Arizona as an associate professor and the director of the Intelligent Systems and Decisions Laboratory. Prof. Zeng is a senior member of IEEE. His research interests include software agents and multi-agent systems, intelligence and security informatics, social computing and recommender systems.
    Peng Su is a Ph.D. candidate at the Institute of Automation, Chinese Academy of Sciences. His research interests mainly focus on machine learning and social computing.
    Fei-Yue Wang received his Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute in 1990. He is the director of the Key Laboratory of Complex Systems and Intelligence Science at the Chinese Academy of Sciences. He is also a professor in the University of Arizona's Systems & Industrial Engineering Department and the director of the university's Program in Advanced Research of Complex Systems. Prof. Wang is a fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. His current research interests include social computing, Web and services science, modeling, analysis and control of complex systems, especially social and cyber-physical systems.
  • Supported by:

    This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 60621001, 60875028, 60875049, and 70890084, the Ministry of Science and Technology of China under Grant No. 2006AA010106, and the Chinese Academy of Sciences under Grant Nos. 2F05N01, 2F08N03 and 2F07C01.

Cultural modeling (CM) is an emergent and promising research area in social computing. It aims to develop behavioral models of human groups and analyze the impact of culture factors on human group behavior using computational methods. Machine learning methods, in particular classification, play a critical role in such applications. Since various cultural-related data sets possess different characteristics, it is important to gain a computational understanding of performance characteristics of various machine learning methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling data set and analyze the experimental results as to group behavior forecasting.

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