软件工程中因变量离散化噪声对机器学习分类器的影响
Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering
摘要：
研究人员通常通过引入人工离散化阈值（例如，中位数）。然而，这种离散化可能引入噪声（即，离散化噪声），这是由于接近人工阈值的数据点的模糊类忠诚度。以往的研究并没有提供一个明确的指导离散化噪声的分类器上的影响，以及如何处理这样的噪声。在本文中，我们提出了一个框架，以帮助研究人员和从业者系统地估计离散化噪声对分类器的影响，其对各种性能指标和分类器的解释的影响。通过对7个软件工程数据集的实例研究，发现：1）离散化噪声对分类器性能指标的影响程度因数据集而异; 2）分类器的解释能力总体上受到离散化噪声的影响，但前3个最重要的特征不受离散化噪声的影响。因此，我们建议从业者和研究人员使用我们的框架来了解离散化噪声对其构建的分类器性能的影响，并估计从数据集中丢弃的离散化噪声的确切数量，以避免此类噪声的负面影响。
英文摘要：
Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.
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