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Springer Handbook of Computational Intelligence pp 349–370Cite as

Rough Set Methodology for Decision Aiding

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Abstract

Since its conception, the dominance-based rough set approach (GlossaryTerm

DRSA

) has been adapted to a large variety of decision problems. In this chapter we outline the rough set methodology designed for multi-attribute decision aiding. GlossaryTerm

DRSA

was proposed as an extension of the Pawlak concept of rough sets in order to deal with ordinal data. We focus on decision problems where all attributes describing objects of a decision problem have ordered value sets (scales). Such attributes are called criteria, and thus the problems are called multi-criteria decision problems. Criteria are real-valued functions of gain or cost type, depending on whether a greater value is better or worse, respectively. In these problems, we also assume the presence of a well defined decision maker (GlossaryTerm

DM

) (single of group GlossaryTerm

DM

) concerned by multi-criteria classification, choice, and ranking.

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Abbreviations

DM:

decision maker

DRSA:

dominance-based rough set approach

GA:

general achievement

IRSA:

indiscernibility-based rough set approach

JMAF:

java multi-criteria and multi-attribute analysis framework

OR:

operational research

VC:

variable consistency

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Authors and Affiliations

  1. Inst. Computing Science, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Roman Słowiński

  2. Dep. Economics and Business, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco & Benedetto Matarazzo

Authors
  1. Roman Słowiński
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  2. Salvatore Greco
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  3. Benedetto Matarazzo
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Correspondence to Roman Słowiński .

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Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Słowiński, R., Greco, S., Matarazzo, B. (2015). Rough Set Methodology for Decision Aiding. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_22

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