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E-CLONALG: An Enhanced Classifier Developed from CLONALG

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Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 556))

Abstract

This paper proposes an improved version of CLONALG, Clone Selection Algorithm based on Artificial Immune System that matches with the conventional classifiers in terms of accuracy tested on the same data sets. Clonal Selection Algorithm is an artificial immune system model. Instead of randomly selecting antibodies, it is proposed to take k memory pools consisting of all the learning cases. Also, an array averaged over the pools is created and is considered for cloning. Instead of using the best clone and calculating the similarity measure and comparing with the original cell, here, k best clones were selected, the average similarity measure was evaluated and noise was filtered. This process enhances the accuracy from 76.9 to 94.2 %, ahead of the conventional classification methods.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology, Kharagpur, West Bengal, India

    Arijit Panigrahy

  2. National Informatics Centre, Berhampur, Odisha, India

    Rama Krushna Das

Authors
  1. Arijit Panigrahy
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  2. Rama Krushna Das
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Corresponding author

Correspondence to Arijit Panigrahy .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering & Information Technology, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of CSE, National Institute of Technology (NIT), Rourkela, Odisha, India

    Durga Prasad Mohapatra

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Panigrahy, A., Das, R.K. (2017). E-CLONALG: An Enhanced Classifier Developed from CLONALG. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_26

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  • DOI: https://doi.org/10.1007/978-981-10-3874-7_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3873-0

  • Online ISBN: 978-981-10-3874-7

  • eBook Packages: EngineeringEngineering (R0)

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