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Rough-Set-Based Decision Support

In: Search Methodologies

Author

Listed:
  • Roman Słowiński

    (Poznań University of Technology, Poznań, and Polish Academy of Sciences, Systems Research Institute)

  • Salvatore Greco

    (University of Catania
    Operations & Systems Management University of Portsmouth)

  • Benedetto Matarazzo

    (University of Catania)

Abstract

In this chapter, we are concerned with the discovery of knowledge from data describing a decision situation. A decision situation is characterized by a set of states or examples, which relate the input with the output of the situation. The aim is to find concise knowledge patterns that summarize a decision situation, and that are useful for explanation of this situation, as well as for the prediction of future similar situations. They are particularly useful in such decision problems as technical or medical diagnostics, performance evaluation and risk assessment. A decision situation is described by a set of attributes, which we might also call properties, features, characteristics, etc. Such attributes may be concerned with either the input or output of a situation or, in other words, with either conditions or decisions. Within this chapter, we will refer to states or examples of a decision situation as objects. The goal of the chapter is to present a knowledge discovery paradigm for multi-attribute and multicriteria decision making, which is based upon the concept of rough sets. Rough set theory was introduced by Pawlak (1982, 1991). Since then, it has often proved to be an excellent mathematical tool for the analysis of a vague description of objects. The adjective vague (referring to the quality of information) is concerned with inconsistency or ambiguity. The rough set philosophy is based on the assumption that with every object of the universe U there is associated a certain amount of information (data, knowledge). This information can be expressed by means of a number of attributes. The attributes describe the object. Objects which have the same description are said to be indiscernible (similar) with respect to the available information. The indiscernibility relation thus generated constitutes the mathematical basis of rough set theory. It induces a partition of the universe into blocks of indiscernible objects, called elementary sets, which can then be used to build knowledge about a real or abstract world. The use of the indiscernibility relation results in information granulation.

Suggested Citation

  • Roman Słowiński & Salvatore Greco & Benedetto Matarazzo, 2014. "Rough-Set-Based Decision Support," Springer Books, in: Edmund K. Burke & Graham Kendall (ed.), Search Methodologies, edition 2, chapter 0, pages 557-609, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6940-7_19
    DOI: 10.1007/978-1-4614-6940-7_19
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    Citations

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    Cited by:

    1. Cinelli, Marco & Kadziński, Miłosz & Gonzalez, Michael & Słowiński, Roman, 2020. "How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy," Omega, Elsevier, vol. 96(C).
    2. Małgorzata Przybyła-Kasperek, 2019. "Comparison Of Two Methods For Generating The Coalitions Of Classifiers And Two Methods For Reducing Dimensionality In A Dispersed Decision-Making System," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-31, June.
    3. Szeląg, Marcin & Słowiński, Roman, 2024. "Explaining and predicting customer churn by monotonic rules induced from ordinal data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 414-424.
    4. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    5. Du, Wen Sheng & Hu, Bao Qing, 2017. "Dominance-based rough fuzzy set approach and its application to rule induction," European Journal of Operational Research, Elsevier, vol. 261(2), pages 690-703.
    6. Przybyła-Kasperek, Małgorzata & Wakulicz-Deja, Alicja, 2016. "The strength of coalition in a dispersed decision support system with negotiations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 947-968.
    7. Angilella, Silvia & Corrente, Salvatore & Greco, Salvatore & Słowiński, Roman, 2016. "Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model," Omega, Elsevier, vol. 63(C), pages 154-169.
    8. Marco Cinelli & Matteo Spada & Miłosz Kadziński & Grzegorz Miebs & Peter Burgherr, 2019. "Advancing Hazard Assessment of Energy Accidents in the Natural Gas Sector with Rough Set Theory and Decision Rules," Energies, MDPI, vol. 12(21), pages 1-17, November.
    9. Kao-Yi Shen, 2017. "Compromise between Short- and Long-Term Financial Sustainability: A Hybrid Model for Supporting R&D Decisions," Sustainability, MDPI, vol. 9(3), pages 1-17, March.

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