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On Generalized Decision Functions: Reducts, Networks and Ensembles

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

Abstract

We summarize our observations on utilizing generalized decision functions to define dependencies between attributes in decision systems. We refer to well-known criteria for attribute selection and less-known results linking generalized decisions with the notions of multivalued dependency and conditional independence. We formulate the problem of finding the simplest ensembles of subsets of attributes which allow to retrieve original decision values of considered objects by intersecting the sets of possible decisions induced by particular attributes.

Partially supported by Polish National Science Centre grants DEC-2012/05/B/ST6/03215 and DEC-2013/09/B/ST6/01568, and by Polish National Centre for Research and Development grants PBS2/B9/20/2013 and O ROB/0010/03/001.

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Authors and Affiliations

  1. Institute of Mathematics, University of Warsaw, Ul. Banacha 2, 02-097, Warsaw, Poland

    Dominik Ślęzak

  2. Infobright Inc., Poland, Ul. Krzywickiego 34 Pok. 219, 02-078, Warsaw, Poland

    Dominik Ślęzak

Authors
  1. Dominik Ślęzak
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Correspondence to Dominik Ślęzak .

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Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Ślęzak, D. (2015). On Generalized Decision Functions: Reducts, Networks and Ensembles. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

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