Abstract
In the context of generalized rough sets, it is possible to introduce in an Information System two different rough approximations. These are induced, respectively, by a Similarity and a Preclusivity relation ([3,4]). It is possible to show that the last one is always better than the first one. Here, we present a quantitative analysis of the relative performances of the two different approximations. The most important conclusion is that preclusive and similar approximation consistently differ when there is a low quality of approximation.
This work has been supported by MIURCOFIN project “Formal Languages and Automata: Theory and Application”.
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Cattaneo, G., Ciucci, D. (2002). A Quantitative Analysis of Preclusivity vs. Similarity Based Rough Approximations. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_9
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DOI: https://doi.org/10.1007/3-540-45813-1_9
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