  In statistical classification and machine learning, as well as in social and
other sciences, a number of measures of association have been proposed for
assessing and comparing individual classifiers, raters, as well as their
groups. In this paper, we introduce, justify, and explore several new measures
of association, which we call CO-, ANTI- and COANTI-correlation coefficients,
that we demonstrate to be powerful tools for classifying confusion matrices. We
illustrate the performance of these new coefficients using a number of
examples, from which we also conclude that the coefficients are new objects in
the sense that they differ from those already in the literature.
