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Functional Correlations in the Pursuit of Performance Assessment of Classifiers
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2020-02-17 , DOI: 10.1142/s0218001420510131
Nadezhda Gribkova 1 , Ričardas Zitikis 2
Affiliation  

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.

中文翻译:

追求分类器绩效评估的功能相关性

在统计分类和机器学习以及社会科学和其他科学中,已经提出了许多关联度量来评估和比较单个分类器、评估者及其组。在本文中,我们介绍、证明和探索了几种新的关联度量,我们称之为 CO-、ANTI- 和 COANTI-相关系数,我们证明它们是分类混淆矩阵的强大工具。我们使用大量示例来说明这些新系数的性能,从中我们还得出结论,这些系数是新对象,因为它们与文献中已有的不同。
更新日期:2020-02-17
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