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Immune Inspired Adaptive Information Filtering: Focusing on Profile Adaptation

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Bio-Inspired Models of Networks, Information, and Computing Systems (BIONETICS 2011)

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Abstract

This paper explores approaches to Adaptive Information Filtering (AIF) in the context of changing user interests. Based on the existing artificial immune system for email classification (AISEC), we demonstrate an effective extension to classification based on the body of emails. Widening this to the problem of AIF on dynamic web content, we propose to explore dynamic clonal selection algorithms (DCSAs) that include dynamically changing thresholds.

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References

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of York, YO10 5DD, UK

    Nurulhuda Firdaus Mohd Azmi, Fiona Polack & Jon Timmis

  2. Dept. of Electronics, University of York, YO10 5DD, UK

    Jon Timmis

  3. Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), International Campus, Kuala Lumpur, Malaysia

    Nurulhuda Firdaus Mohd Azmi

Authors
  1. Nurulhuda Firdaus Mohd Azmi
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  2. Fiona Polack
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  3. Jon Timmis
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Editor information

Editors and Affiliations

  1. School of Computing, Napier University, Merchiston Campus, 10 Colinton Road, EH10 5DT, Edinburgh, UK

    Emma Hart

  2. Department of Computer Science and Department of Electronics, University of York, Heslington, YO10 5DD, York, UK

    Jon Timmis  & Paul Mitchell  & 

  3. Frontier Research Base for Global Young Researchers, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, 565-0871, Suita, Osaka, Japan

    Takadash Nakamo

  4. University of California, 90095, Los Angeles, CA, USA

    Foad Dabiri

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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Mohd Azmi, N.F., Polack, F., Timmis, J. (2012). Immune Inspired Adaptive Information Filtering: Focusing on Profile Adaptation. In: Hart, E., Timmis, J., Mitchell, P., Nakamo, T., Dabiri, F. (eds) Bio-Inspired Models of Networks, Information, and Computing Systems. BIONETICS 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32711-7_24

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  • DOI: https://doi.org/10.1007/978-3-642-32711-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32710-0

  • Online ISBN: 978-3-642-32711-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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