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The role of early stopping and population size in XCS for intrusion detection

Published: 15 October 2006 Publication History

Abstract

Evolutionary Learning Classifier Systems (LCSs) are rule based systems that have been used effectively in concept learning. XCS is a prominent LCS that uses genetic algorithms and reinforcement learning techniques. In traditional machine learning (ML), early stopping has been investigated extensively to the extent that it is now a default mechanism in many systems. However, there has been a belief that EC methods are more resilient to overfitting. Therefore, this topic is under-investigated in the evolutionary computation literature and has not been investigated in LCS. In this paper, we show that it is necessary to stop evolution in LCS using a stopping criteria other than a maximum number of generations and that evolution may suffer from overfitting similar to other ML methods.

References

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E. Bernadó, X. Llorà, and J. M. Garrell. XCS and GALE: a comparative study of two learning classifier systems with six other learning algorithms on classification tasks. In Proceedings of the 4th International Workshop on Learning Classifier Systems (IWLCS-2001), pages 337-341, 2001.
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K. Shafi, H. Abbass, and W. Zhu. The role of early stopping and population size in XCS for intrusion detection. Technical Report TR-ALAR-200604006, Defence and Security Applications Research Centre, University of New South Wales @ ADFA, Canberra, Australia, 2006.
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    Published In

    cover image Guide Proceedings
    SEAL'06: Proceedings of the 6th international conference on Simulated Evolution And Learning
    October 2006
    940 pages
    ISBN:3540473319
    • Editors:
    • Tzai-Der Wang,
    • Xiaodong Li,
    • Shu-Heng Chen,
    • Xufa Wang,
    • Hussein Abbass

    Sponsors

    • NSF of China: National Natural Science Foundation of China
    • Anhui Computer Federation
    • University of Science and Technology of China: University of Science and Technology of China
    • Anhui Province Key Laboratory for Computing and Communication Software Engineering: Anhui Province Key Laboratory for Computing and Communication Software Engineering
    • Chinese Academy of Sciences

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    Springer-Verlag

    Berlin, Heidelberg

    Publication History

    Published: 15 October 2006

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