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International Workshop on Fuzzy Logic and Applications

WILF 2016: Fuzzy Logic and Soft Computing Applications pp 116–125Cite as

Feature Selection Through Composition of Rough–Fuzzy Sets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10147))

Abstract

The well known principle of curse of dimensionality links both dimensions of a dataset stating that as dimensionality increases samples become too sparse to effectively extract knowledge. Hence dimensionality reduction is essential when there are many features and not sufficient samples. We describe an algorithm for unsupervised dimensionality reduction that exploits a model of the hybridization of rough and fuzzy sets. Rough set theory and fuzzy logic are mathematical frameworks for granular computing forming a theoretical basis for the treatment of uncertainty in many real–world problems. The hybrid notion of rough fuzzy sets comes from the combination of these two models of uncertainty and helps to exploit, at the same time, properties like coarseness and vagueness. Experimental results demonstrated that the proposed approach can effectively reduce dataset dimensionality whilst retaining useful features when class labels are unknown or missing.

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Author information

Authors and Affiliations

  1. Department of Science and Technology, University of Naples “Parthenope”, Centro Direzionale Isola C4, 80143, Naples, Italy

    Alessio Ferone & Alfredo Petrosino

Authors
  1. Alessio Ferone
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  2. Alfredo Petrosino
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Corresponding author

Correspondence to Alessio Ferone .

Editor information

Editors and Affiliations

  1. University of Naples “Parthenope”, Naples, Italy

    Alfredo Petrosino

  2. University of Salerno, Fisciano, (Salerno), Italy

    Vincenzo Loia

  3. University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

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Ferone, A., Petrosino, A. (2017). Feature Selection Through Composition of Rough–Fuzzy Sets. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-52962-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52961-5

  • Online ISBN: 978-3-319-52962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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