Abstract
In this work, we pursue the theme of applications of rough mereology, presenting a scheme for classifier construction by voting of training objects, exhaustive set of rules, and granules of training objects according to weights assigned by residual rough inclusions. The results show a high effectiveness of this approach as witnessed by the reported tests with some well–known data sets from UCI repository whose results are compared against the standard rough set exhaustive classifier.
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UCI Repository, http://www.ics.uci.edu/~mlearn/databases/
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Polkowski, L., Artiemjew, P. (2008). Rough Mereology in Classification of Data: Voting by Means of Residual Rough Inclusions. 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_12
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DOI: https://doi.org/10.1007/978-3-540-88425-5_12
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