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. © 2010 Springer-Verlag Berlin Heidelberg.
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Skowron, A., & Stepaniuk, J. (2010). Approximations and classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6086 LNAI, pp. 297–306). https://doi.org/10.1007/978-3-642-13529-3_32
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