Abstract

In this paper, we propose a new function for estimating the quality of classification into N classes. This function is invariant to the imbalance of classes to be processed. It is constructed by computing the sine of an angle formed by the errors of each class in an N-dimensional space. A geometrical substantiation of its construction is provided and its properties are investigated. It is shown that this function is an improved version of the balanced accuracy function. In contrast to other functions, the proposed function considers class distribution of errors. Examples of analyzing the confusion matrices in the classification of synthetic and real-world data are provided.

中文翻译：

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估计不平衡数据分类结果的新功能

摘要

在本文中，我们提出了一个新的函数，用于估计N类分类的质量。该函数对于要处理的类的不平衡是不变的。它是通过计算N维空间中每个类别的误差所形成的角度的正弦值来构造的。提供了其构造的几何证明，并研究了其性能。结果表明，该功能是平衡精度功能的改进版本。与其他功能相反，提出的功能考虑了错误的类别分布。提供了在合成数据和实际数据分类中分析混淆矩阵的示例。