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Rough Fuzzy Integrals for Information Fusion and Classification

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Rough Sets and Current Trends in Computing (RSCTC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3066))

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Abstract

This paper presents two extended fuzzy integrals under rough uncertainty, i.e. rough upper fuzzy and lower fuzzy integrals, and extended properties are also given. Furthermore, these two integrals are applied here in information fusion and classification processes for rough features, and the corresponding extended models are also proposed. These types of integrals generalize fuzzy integrals and enlarge their domains of applications in fusion and classification under rough uncertainty. Examples show that they fuse or classify objects with rough features with fairly good effects while the existed methods based on reals can not solve.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Tao Guan & Boqin Feng

Authors
  1. Tao Guan
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  2. Boqin Feng
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Editor information

Editors and Affiliations

  1. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

  3. The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden

    Jan Komorowski

  4. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

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© 2004 Springer-Verlag Berlin Heidelberg

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Guan, T., Feng, B. (2004). Rough Fuzzy Integrals for Information Fusion and Classification. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_43

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  • DOI: https://doi.org/10.1007/978-3-540-25929-9_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22117-3

  • Online ISBN: 978-3-540-25929-9

  • eBook Packages: Springer Book Archive

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