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Feb, 2025
评估数据增强引起的机器学习模型训练和测试偏差
Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models
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Riddhi More, Jeremy S. Bradbury
TL;DR
本研究解决了在训练和测试机器学习模型时,数据增强对模型偏差影响的理解不足的问题。通过对不稳定测试分类的案例研究,提出了一种测试偏差的方法,并阐明了增强样本在测试集中的影响。这项工作有助于提高软件工程中模型评估的准确性,尤其是在数据稀缺的特定领域。
Abstract
Data Augmentation
has become a standard practice in
Software Engineering
to address limited or imbalanced data sets, particularly in specialized domains like test classification and bug detection where data can b
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