Rough Mereology in Classification of Data: Voting by Means of Residual Rough Inclusions

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.