Functional Correlations in the Pursuit of Performance Assessment of Classifiers
This work provides incremental improvements in performance assessment for classifiers in statistical classification and machine learning.
The paper tackles the problem of assessing and comparing classifiers by introducing new measures of association called CO-, ANTI-, and COANTI-correlation coefficients, which are shown to be effective tools for classifying confusion matrices through illustrative examples.
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.