Rule+Exception Learning-Based Class Specification and Labeling in Intelligence and Security Analysis

  • Jue Wang
  • Fei-Yue Wang
  • Daniel D. Zeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3917)


One of the key tasks in intelligence and security informatics (ISI) is to find anomalies and exceptions from typically voluminous datasets and observations [1-4]. For example, in the context of societal security, finding “exceptions” may involve discovering irregular behaviors in a community where it is assumed that most people behave normally.


Human Agent Security Analysis Homeland Security Reduct Theory Require Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Chen, H., Wang, F.Y.: Artificial Intelligence for Homeland Security. IEEE Intelligent Systems, pp. 12-16 (2005)Google Scholar
  2. 2.
    Chen, H., Wang, F.Y., Zeng, D.: Intelligence and Security informatics for Homeland Security: Information, Communication and Transportation. IEEE Trans. Intelligent Transportation Systems, pp. 329-341 (2004)Google Scholar
  3. 3.
    Kantor, P., Muresan, G., Roberts, F., Zeng, D., Wang, F.Y., Chen, H., Merkle, R.C.: Intelligence and Security Informatics. In: Proceedings IEEE Conference on Intelligence and Security informatics, Atlanta, GA,USA (May 2005)Google Scholar
  4. 4.
    Yao, Y.Y., Wang, F.Y., Wang, J., Zeng, D.: Rule + Exception Strategies for Security Information Analysis. IEEE Intelligent Systems, 52–57 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jue Wang
    • 1
  • Fei-Yue Wang
    • 1
  • Daniel D. Zeng
    • 2
  1. 1.Chinese Academy of SciencesBeijingChina
  2. 2.University of ArizonaTucsonUSA

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