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Toward Qualitative Assessment of Rough Sets in Terms of Decision Attribute Values in Simple Decision Systems over Ontological Graphs

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Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8988))

Abstract

Approximation of sets is a fundamental notion of rough set theory (RST) proposed by Z. Pawlak. Each rough set can be characterized numerically by the coefficient called the accuracy of approximation. This coefficient determines quantitatively a degree of roughness. Such an approach does not take into consideration semantics of data. In the paper, we show that adding information on semantic relations between decision attribute values in the form of ontological graphs enables us to determine qualitatively the accuracy of approximation. The qualitative assessment of approximation should be treated as some additional characteristic of rough sets. The proposed approach enriches application of rough sets if decision attribute values classifying objects are symbolical (e.g., words, terms, linguistic concepts, etc.). The presented approach refers to a general trend in computations proposed by L. Zadeh and called “computing with words”.

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Acknowledgments

The author would like to thank the anonymous reviewers for their critical remarks and useful suggestions that greatly contributed to improving the final version of the paper.

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Correspondence to Krzysztof Pancerz .

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Pancerz, K. (2015). Toward Qualitative Assessment of Rough Sets in Terms of Decision Attribute Values in Simple Decision Systems over Ontological Graphs. In: Peters, J., Skowron, A., Ślȩzak, D., Nguyen, H., Bazan, J. (eds) Transactions on Rough Sets XIX. Lecture Notes in Computer Science(), vol 8988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47815-8_6

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  • DOI: https://doi.org/10.1007/978-3-662-47815-8_6

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