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A new data pre-processing approach for the dendritic cellalgorithm based on fuzzy rough set theory

Published: 06 July 2013 Publication History

Abstract

The aim of this paper is to develop a new data pre-processing method for the dendritic cell algorithm (DCA) based on Fuzzy Rough Set Theory (FRST). In this new fuzzy-rough model, the data pre-processing phase is based on the fuzzy positive region and the fuzzy dependency degree concepts. Results show that applying FRST is more convenient for the DCA data pre-processing phase yielding much better performance in terms of accuracy.

References

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J. Greensmith, U. Aickelin, and S. Cayzer. Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In ICARIS, pages 153--167, 2005.
[2]
Z. Chelly and Z. Elouedi. Rc-dca: A new feature selection and signal categorization technique for the dendritic cell algorithm based on rough set theory. In ICARIS, pages 152--165, 2012.
[3]
Z. Pawlak. Rough sets. International Journal of Computer and Information Science, 11:341--356, 1982.
[4]
D. Dubois and H. Prade. Putting rough sets and fuzzy sets together. Kluwer Academic Publishers, Dordrecht, 1992.
[5]
A. Asuncion and D.J. Newman. UCI machine learning repository, http://mlearn.ics.uci.edu/mlrepository.html, 2007.
[6]
J. Greensmith and U. Aickelin. The deterministic dendritic cell algorithm. In ICARIS, pages 291--302, 2008.

Cited By

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  • (2016)An upper and lower CUSUM for signal normalization in the dendritic cell algorithmEvolutionary Intelligence10.1007/s12065-016-0136-39:1-2(37-51)Online publication date: 20-Apr-2016
  • (2016)A survey of the dendritic cell algorithmKnowledge and Information Systems10.1007/s10115-015-0891-y48:3(505-535)Online publication date: 1-Sep-2016
  • (2014)A two-leveled hybrid dendritic cell algorithm under imprecise reasoningProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598217(97-104)Online publication date: 12-Jul-2014
  • Show More Cited By

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  1. A new data pre-processing approach for the dendritic cellalgorithm based on fuzzy rough set theory

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    Published In

    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. artificial immune systems
    2. dendritic cells
    3. feature selection
    4. fuzzy-rough sets

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    GECCO '13
    Sponsor:
    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    Cited By

    View all
    • (2016)An upper and lower CUSUM for signal normalization in the dendritic cell algorithmEvolutionary Intelligence10.1007/s12065-016-0136-39:1-2(37-51)Online publication date: 20-Apr-2016
    • (2016)A survey of the dendritic cell algorithmKnowledge and Information Systems10.1007/s10115-015-0891-y48:3(505-535)Online publication date: 1-Sep-2016
    • (2014)A two-leveled hybrid dendritic cell algorithm under imprecise reasoningProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598217(97-104)Online publication date: 12-Jul-2014
    • (2014)Improving the Dendritic Cell Algorithm Performance Using Fuzzy-Rough Set Theory as a Pattern Discovery TechniqueProceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 201410.1007/978-3-319-08156-4_3(23-32)Online publication date: 2014

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