论文标题
基于召回和选择性归一化的不平衡数据的分类性能指标
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels
论文作者
论文摘要
在类别不平衡数据集的分类中，用于模型选择和与竞争方法进行比较的性能度量是一个主要问题。为了克服这个问题，对不平衡比率的几个角度定义和分析了几种绩效指标。仍然没有明确的指示指标是通用的，可以用于任何偏斜的数据问题。在本文中，我们基于召回和选择性的谐波平均值引入了一种新的绩效指标。本文表明，所提出的性能度量具有适合不平衡数据集的正确属性。特别是，在大多数类示例定义的空间和不平衡之比中，它对多数级别的变化不太敏感，并且与其他现有的单价绩效指标相比，对少数类别的变化更敏感。此外，通过分析证明了其他绩效指标的身份。
In the classification of a class imbalance dataset, the performance measure used for the model selection and comparison to competing methods is a major issue. In order to overcome this problem several performance measures are defined and analyzed in several perspectives regarding in particular the imbalance ratio. There is still no clear indication which metric is universal and can be used for any skewed data problem. In this paper we introduced a new performance measure based on the harmonic mean of Recall and Selectivity normalized in class labels. This paper shows that the proposed performance measure has the right properties for the imbalanced dataset. In particular, in the space defined by the majority class examples and imbalance ratio it is less sensitive to changes in the majority class and more sensitive to changes in the minority class compared with other existing single-value performance measures. Additionally, the identity of the other performance measures has been proven analytically.
