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Improving the Generalization Capability of Hybrid Immune Detector Maturation Algorithm

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Hybrid Artificial Intelligent Systems (HAIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7208))

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Abstract

In this work, an augmented hybrid immune detector maturation algorithm applied in anomaly detection is proposed to improve the generalization capability. Experiment results show the algorithm is more effective and its generalization capability to detect more similar patterns is improved.

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References

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Author information

Authors and Affiliations

  1. Electronic Information Department, Zhejiang Wanli University, No.8 South Qian Hu Road, Ningbo, Zhejiang, 315100, China

    Jungan Chen, Feng Liang & Zhaoxi Fang

Authors
  1. Jungan Chen
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  2. Feng Liang
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  3. Zhaoxi Fang
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Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VÅ B-TU Ostrava 17, Listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. Machine Intelligence Research Labs(MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071, Auburn, Washington, USA

    Ajith Abraham

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of the Basque Country, Pº Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Manuel Graña

  6. Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, 120-749, Seoul, Korea

    Sung-Bae Cho

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© 2012 Springer-Verlag Berlin Heidelberg

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Chen, J., Liang, F., Fang, Z. (2012). Improving the Generalization Capability of Hybrid Immune Detector Maturation Algorithm. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_27

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  • DOI: https://doi.org/10.1007/978-3-642-28942-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28941-5

  • Online ISBN: 978-3-642-28942-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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