Granular computing in the frame of rough mereology. A case study: Classification of data into decision categories by means of granular reflections of data

This work extends the authors' contribution to the International Conference on Rough Sets and Current Trends in Computing (RSCTC 2008) held at the University of Akron in October 2008. It is dedicated to the topic of granular computing, formalized within the theory of rough mereology, as proposed by Polkowski; as an application of the idea of a granular reflection of data and of classifiers induced from it (Polkowski, 2005), we give an account of recent results in this area. A scheme for classifier construction based on factoring a classifier through a granular reflection of data; voting by granules of training objects; voting by granules of decision rules induced from the training set; voting by granules induced from a granular reflection of data, is presented here. In voting cases, voting is based on weights computed by means of rough inclusions induced from residual implications of continuous t‐norms. The results show a high effectiveness of this approach as witnessed by the reported tests with some well‐‐known data sets from University of California, at Irvine (UCI) repository whose results are compared against the standard rough set exhaustive classifier whose accuracy is indicated under the radius of “nil.” © 2011 Wiley Periodicals, Inc.