  A widespread approach in machine learning to evaluate the quality of a
classifier is to cross -- classify predicted and actual decision classes in a
confusion matrix, also called error matrix. A classification tool which does
not assume distributional parameters but only information contained in the data
is based on the rough set data model which assumes that knowledge is given only
up to a certain granularity. Using this assumption and the technique of
confusion matrices, we define various indices and classifiers based on rough
confusion matrices.
