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A New Hybrid Fuzzy-Rough Dendritic Cell Immune Classifier

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Advances in Swarm Intelligence (ICSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7928))

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Abstract

The Dendritic Cell Algorithm (DCA) is an immune-inspired classification algorithm based on the behavior of natural dendritic cells (DC). This paper proposes a novel version of the DCA based on a two-level hybrid fuzzy-rough model. In the top-level, the proposed algorithm, named RST-MFDCM, applies rough set theory to build a solid data pre-processing phase. In the second level, RST-MFDCM applies fuzzy set theory to smooth the crisp separation between the DC’s semi-mature and mature contexts. The experimental results show that RST-MFDCM succeeds in obtaining significantly improved classification accuracy.

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Author information

Authors and Affiliations

  1. LARODEC, Institut Supérieur de Gestion de Tunis, Tunis, Tunisia

    Zeineb Chelly & Zied Elouedi

Authors
  1. Zeineb Chelly
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  2. Zied Elouedi
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), School of lectronics Engineering and Computer Science, Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China

    Yuhui Shi

  3. Automation College, Harbin Engineering University, 150001, Harbin, China

    Hongwei Mo

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Chelly, Z., Elouedi, Z. (2013). A New Hybrid Fuzzy-Rough Dendritic Cell Immune Classifier. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_60

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  • DOI: https://doi.org/10.1007/978-3-642-38703-6_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38702-9

  • Online ISBN: 978-3-642-38703-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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