Abstract
In this paper we present a new algebraic model for rough set theory that permits to distinguish between two kinds of “imperfect” information: on one hand, vagueness due to imprecise knowledge and uncertainty typical of fuzzy sets, and on the other hand, ambiguity due to indiscernibility and coarseness typical of rough sets. In other words, we wish to distinguish between fuzziness and granularity of information. To build our model we are using the Brouwer-Zadeh lattice representing a basic vagueness or uncertainty, and to introduce rough approximation in this context, we define a new operator, called Pawlak operator. The new model we obtain in this way is called Pawlak-Brouwer-Zadeh lattice. Analyzing the Pawlak-Brouwer-Zadeh lattice, and discussing its relationships with the Brouwer-Zadeh lattices, we obtain some interesting results, including some representation theorems, that are important also for the Brouwer-Zadeh lattices.
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Greco, S., Matarazzo, B., Słowiński, R. (2012). Distinguishing Vagueness from Ambiguity by Means of Pawlak-Brouwer-Zadeh Lattices. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_63
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DOI: https://doi.org/10.1007/978-3-642-31709-5_63
Publisher Name: Springer, Berlin, Heidelberg
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