Abstract
The paradigm of Granular Computing has quite recently emerged as an area of research on its own; in particular, it is pursued within rough set theory initiated by Zdzisław Pawlak. Granules of knowledge consist of entities with a similar in a sense information content. An idea of a granular counterpart to a decision/information system has been put forth, along with its consequence in the form of the hypothesis that various operators, aimed at dealing with information, should factorize sufficiently faithfully through granular structures [7], [8]. Most important such operators are algorithms for inducing classifiers. We show results of testing few well-known algorithms for classifier induction on well–used data sets from Irvine Repository in order to verify the hypothesis. The results confirm the hypothesis in case of selected representative algorithms and open a new prospective area of research.
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Polkowski, L., Artiemjew, P. (2007). On Granular Rough Computing: Factoring Classifiers Through Granulated Decision Systems. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_30
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DOI: https://doi.org/10.1007/978-3-540-73451-2_30
Publisher Name: Springer, Berlin, Heidelberg
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