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The Notion of Pre-aggregation Function

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Modeling Decisions for Artificial Intelligence (MDAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9321))

Abstract

In this work we consider directional monotone functions and use this idea to introduce the notion of pre-aggregation function. In particular, we propose an example of such functions inspired on Choquet integrals.

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Notes

  1. 1.

    In this paper, an increasing (decreasing) function does not need to be strictly increasing (decreasing).

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Acknowledgment

This work is supported by CNPq (Proc. 305131/10-9, 481283/2013-7, 306970/2013-9), NSF (ref. TIN2013-40765-P, TIN2011-29520) and grant APVV-14-0013.

Author information

Authors and Affiliations

  1. Centro de Ciencias Computacionais, Universidade Federal do Rio Grande, Rio Grande, Brazil

    Giancarlo Lucca & Graçaliz Pereira Dimuro

  2. Departamento of Automática y Computación and with the Institute of Smart Cities, Universidad Publica de Navarra, 31006, Navarra, Spain

    José Antonio Sanz & Humberto Bustince

  3. Departamento de Informática e Matemática Aplicada, Universidade Federal do Rio Grande do Norte, Natal, Brazil

    Benjamín Bedregal

  4. Slovak University of Technology, Radlinskeho 11, Bratislava, Slovakia

    Radko Mesiar

  5. Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, 18208, Prague, Czech Republic

    Radko Mesiar

  6. Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology, 810 05, Bratislava, Slovakia

    Anna Kolesárová

Authors
  1. Giancarlo Lucca
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  2. José Antonio Sanz
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  3. Graçaliz Pereira Dimuro
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  4. Benjamín Bedregal
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  5. Radko Mesiar
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  6. Anna Kolesárová
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  7. Humberto Bustince
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Corresponding author

Correspondence to Humberto Bustince .

Editor information

Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenc Torra

  2. Toho Gakuen, Tokyo, Japan

    Torra Narukawa

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Lucca, G. et al. (2015). The Notion of Pre-aggregation Function . In: Torra, V., Narukawa, T. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2015. Lecture Notes in Computer Science(), vol 9321. Springer, Cham. https://doi.org/10.1007/978-3-319-23240-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-23240-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23239-3

  • Online ISBN: 978-3-319-23240-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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