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UK Workshop on Computational Intelligence

UKCI 2019: Advances in Computational Intelligence Systems pp 529–540Cite as

Signal Categorisation for Dendritic Cell Algorithm Using GA with Partial Shuffle Mutation

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1043)

Abstract

Dendritic Cell Algorithm (DCA) is a bio-inspired system which was specifically developed for anomaly detection problems. In its preprocessing phase, the conventional DC requires domain or expert knowledge to manually categorise the input features for a given dataset into three signal categories termed as safe signal, pathogenic associated molecular pattern and danger signal. The manual preprocessing phase often over-fits the data to the algorithm, which is undesirable. The principal component analysis (PCA) and fuzzy-rough set theory (FRST) based-DCA techniques have been proposed to overcome the aforementioned limitation by automatically categorising the input features to their convenient signal categories. However, the PCA destroys the underlying meaning behind the initial features presented in the input dataset and generates poor classification performance, whilst FRST-DCA is only practical for very simple datasets. Therefore, this study investigates the employment of Genetic Algorithm based on Partial Shuffle Mutation to automatically categorise the input features into the three signal categories. The experimental results of the proposed approach on eleven benchmark datasets have revealed its superiority over other versions of DCA in terms of accuracy, sensitivity and specificity.

Keywords

  • Dendritic Cell Algorithm
  • Signal categorisation
  • Genetic Algorithm
  • Partial shuffle mutation
  • Features-to-Signal mapping

This work has been supported by the Commonwealth Scholarship Commission (CSC-TZCS-2017-717) and Northumbria University, UK.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

    Noe Elisa & Longzhi Yang

  2. Cognitive Science Department, School of Information Science and Engineering, Xiamen University, Xiamen, China

    Fei Chao

Authors
  1. Noe Elisa
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  2. Longzhi Yang
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  3. Fei Chao
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Corresponding author

Correspondence to Longzhi Yang .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Dr. Zhaojie Ju

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Dr. Longzhi Yang

  3. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

    Dr. Chenguang Yang

  4. School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK

    Alexander Gegov

  5. School of Computing, University of Portsmouth, Portsmouth, UK

    Dalin Zhou

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Elisa, N., Yang, L., Chao, F. (2020). Signal Categorisation for Dendritic Cell Algorithm Using GA with Partial Shuffle Mutation. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_44

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