Abstract
In data sets/decision systems, written down as pairs (U,A ∪ {d}) with objects U, attributes A, and a decision d, objects are described in terms of attribute–value formulas. This representation gives rise to a calculus in terms of descriptors which we call a natural computing. In some recent papers, the idea of L. Polkowski of computing with granules induced from similarity measures called rough inclusions have been tested. In this work, we pursue this topic and we study granular structures resulting from rough inclusions with classification problem in focus. Our results show that classifiers obtained from granular structures give better quality of classification than natural exhaustive classifiers.
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References
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Artiemjew, P. (2008). Natural versus Granular Computing: Classifiers from Granular Structures. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_16
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DOI: https://doi.org/10.1007/978-3-540-88425-5_16
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