Imbalance accuracy metric for model selection in multi-class imbalance classification problems

Mortaz Ebrahim

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Imbalance accuracy metric for model selection in multi-class imbalance classification problems

机译：多级不平衡分类问题中模型选择的不平衡精度度量

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The overall accuracy, macro precision, macro recall, F-score and class balance accuracy, due to their simplicity and easy interpretation, have been among the most popular metrics to measure the performance of classifiers on multi-class problems. However, on imbalance datasets, some of these metrics can be unfairly influenced by heavier classes. Therefore, it is recommended that they are used as a group and not individually. This strategy can unnecessarily complicate the model selection and evaluation in imbalance datasets. In this paper, we introduce a new metric, imbalance accuracy metric (IAM), that can be used as a solo measure for model evaluation and selection. The IAM is built up on top of the existing metrics, is simple to use, and easy to interpret. This metric is meant to be used as a bottom-line measure aiming to eliminate the need for group metric computation and simplify the model selection. (C) 2020 Elsevier B.V. All rights reserved.

机译：由于其简单性和简单的解释，整体准确性，宏精度，宏观调用，F分和阶层平衡准确性一直是最受欢迎的度量标准，以衡量对多级问题的分类器的性能。但是，在不平衡数据集上，这些指标中的一些可能受到较重阶级的影响。因此，建议将它们用作组而不是单独使用。此策略可以不必要地使不平衡数据集中的模型选择和评估复杂化。在本文中，我们介绍了一种新的公制，不平衡精度度量（IAM），可用作模型评估和选择的独奏措施。 IAM建立在现有度量的顶部，使用简单，易于解释。该度量标准旨在用作底线测量，旨在消除对组度量计算的需要并简化模型选择。（c）2020 Elsevier B.v.保留所有权利。

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来源
《Knowledge-Based Systems》 |2020年第27期|106490.1-106490.8|共8页
作者
Mortaz Ebrahim;
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Pace Univ Lubin Sch Business One Pace Plaza New York NY 10038 USA;

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正文语种 eng
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关键词
Machine learning; Classification accuracy; Multi-class problems; Imbalance datasets; Knowledge discovery;

机译：机器学习;分类准确性;多级问题;不平衡数据集;知识发现;

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机译：基于局部和全局指标融合的多类不平衡网络流量数据集的面向类特征选择方法（第168卷，第365页，2015年）
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机译：基于局部和全局度量融合的多类不平衡网络流量数据集的面向类特征选择方法
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机译：基于多分类器系统和特征选择的自适应集成分类算法对多类不平衡数据进行分类
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机译：多不平衡：开源Python工具箱，用于多级不平衡分类
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机译：用于不平衡分类的多类ROC随机森林
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机译：贝叶斯模型平均：开发用于微阵列数据的改进的多类基因选择和分类工具

Imbalance accuracy metric for model selection in multi-class imbalance classification problems

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