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Approximation by Filter Functions

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Rough Sets (IJCRS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11103))

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Abstract

In this exploratory article, we draw attention to the common formal ground among various estimators such as the belief functions of evidence theory and their relatives, approximation quality of rough set theory, and contextual probability. The unifying concept will be a general filter function composed of a basic probability and a weighting which varies according to the problem at hand. To compare the various filter functions we conclude with a simulation study with an example from the area of item response theory.

The ordering of authors is alphabetical and equal authorship is implied.

Ivo Düntsch gratefully acknowledges support by Fujiang Normal Univeristy, the Natural Sciences and Engineering Research Council of Canada Discovery Grant 250153, and by the Bulgarian National Fund of Science, contract DN02/15/19.12.2016.

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Notes

  1. 1.

    The tables and the R-source of the simulation procedure are available for download at www.roughsets.net.

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Acknowledgement

We are grateful to the referees for constructive comments.

Author information

Authors and Affiliations

  1. School of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, China

    Ivo Düntsch & Hui Wang

  2. Brock University, St. Catharines, ON, L2S 3A1, Canada

    Ivo Düntsch

  3. Institut für Evaluation und Marktanalysen, Brinkstr. 19, 49143, Jeggen, Germany

    Günther Gediga

  4. School of Computing and Mathematics, Ulster University, Newtownabbey, Northern Ireland

    Hui Wang

Authors
  1. Ivo Düntsch
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  2. Günther Gediga
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  3. Hui Wang
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Corresponding author

Correspondence to Ivo Düntsch .

Editor information

Editors and Affiliations

  1. University of Warsaw, Warsaw, Poland

    Hung Son Nguyen

  2. Faculty of Information Technology, Vietnam National University, Hanoi, Vietnam

    Quang-Thuy Ha

  3. School of Information Science, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  4. Institute of Computer Science, University of Silesia, Sosnowiec, Poland

    Małgorzata Przybyła-Kasperek

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Düntsch, I., Gediga, G., Wang, H. (2018). Approximation by Filter Functions. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-99368-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99367-6

  • Online ISBN: 978-3-319-99368-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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