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Dominance-Based Rough Set Approach to Reasoning about Ordinal Data - A Tutorial

  • Roman Słowiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5009)

Abstract

This tutorial lecture intends to introduce the Dominance-based Rough Set Approach (DRSA) methodology for reasoning about ordinal data. DRSA, proposed by Greco, Matarazzo and Słowiński (see e.g. [2,6]), extends the classical Rough Set approach by handling background knowledge about ordinal evaluations of objects and about monotonic relationships between these evaluations. In DRSA, the indiscernibility or tolerance relation among objects, which is used in the classical Rough Set approach, has been replaced by the dominance relation – the only relation uncontested in multiattribute pairwise comparisons when attribute scales are ordered. The lecture starts with principles of DRSA and goes through the application of DRSA to fuzzy-rough hybridization [1], to end with DRSA to case-based reasoning [3], which builds on this hybridization. This tutorial prepares the ground for a second tutorial lecture on applications of DRSA to decision analysis.

Keywords

Credit Rating Ordinal Data Decision Attribute Monotonic Relationship Granular Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Greco, S., Inuiguchi, M., Słowiński, R.: Fuzzy rough sets and multiple-premise gradual decision rules, Internat. J. of Approximate Reasoning. 41, 179–211 (2006)zbMATHCrossRefGoogle Scholar
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    Greco, S., Matarazzo, B., Słowiński, R.: Dominance-based Rough Set Approach to Case-Based Reasoning, in V. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds.) MDAI 2006. LNCS (LNAI), vol. 3885, pp. 7–18. Springer, Heidelberg (2006)CrossRefGoogle Scholar
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    Słowiński, R., Greco, S., Matarazzo, B.: Rough set based decision support, ch. 16. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 475–527. Springer, New York (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Roman Słowiński
    • 1
  1. 1.Institute of Computing SciencePoznań University of Technology, 60-965 Poznań, and Systems Research Institute, Polish Academy of SciencesWarsawPoland

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