New Function for Estimating Imbalanced Data Classification Results
В. В. Старовойтов, Yu. I. Golub · Pattern Recognition and Image Analysis · 2020
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.