Affiliation:
1. Faculty of Mathematics and Mechanics, St. Petersburg State University, St. Petersburg 199034, Russia
2. School of Mathematical and Statistical Sciences, Western University, London, ON, Canada N6A 5B7, Canada
Abstract
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.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
1 articles.
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