Abstract
We discuss some important issues for applications that are related to generalizations of the 1994 approximation space definition [11]. In particular, we present examples of rough set based strategies for extension of approximation spaces from samples of objects onto the whole universe of objects. This makes it possible to present methods for inducing approximations of concepts or classifications analogously to the approaches for inducing classifiers known in machine learning or data mining.
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The research has been partially supported by the grants N N516 077837, N N516 069235, and N N516 368334 from Ministry of Science and Higher Education of the Republic of Poland.
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Skowron, A., Stepaniuk, J. (2010). Approximations and Classifiers. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_32
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DOI: https://doi.org/10.1007/978-3-642-13529-3_32
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