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New Function for Estimating Imbalanced Data Classification Results

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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.

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Authors and Affiliations

  1. United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012, Minsk, Belarus

    V. V. Starovoitov & Yu. I. Golub

Authors
  1. V. V. Starovoitov
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  2. Yu. I. Golub
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Corresponding authors

Correspondence to V. V. Starovoitov or Yu. I. Golub.

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The authors declare that they do not have a conflict of interest.

Additional information

Starovoitov Valery, Dr. Sci. (Eng.), Professor, laureate of State Prize of the Republic of Belarus, Chief Researcher, the United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Minsk, Belarus.

Golub Yuliya, Cand. Sci. (Eng.), Associate Professor, Senior Research, the United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Minsk, Belarus.

Translated by Yu. Kornienko

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Starovoitov, V.V., Golub, Y.I. New Function for Estimating Imbalanced Data Classification Results. Pattern Recognit. Image Anal. 30, 295–302 (2020). https://doi.org/10.1134/S105466182003027X

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  • DOI: https://doi.org/10.1134/S105466182003027X

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