Wrapper Methods to Correct Mislabelled Training Data

Jonathan Young, J. Ashburner, S. Ourselin
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引用次数: 14

Abstract

Machine learning has obvious applications to the diagnosis of disease, and for many neurological conditions features extracted from brain images allow classifiers based on neuroimaging biomarkers to provide a useful complement to more traditional diagnostic methods based on symptoms and psychological testing. However the labels used in the training of such systems frequently depend on standard clinical diagnostic methods, meaning they are not completely reliable in many cases. This uncertainty makes the problems this causes hard to study, as it is difficult to measure both the extent of mislabelling and its effect on results. To avoid this problem, we perform classification of gender based on imaging, as this is definitely known for each subject. We then deliberately make known proportions of the training labels incorrect. This allows us to assess the effect of the level of label noise on classification accuracy, and evaluate methods that allow for the mislabelled data. The methods are wrappers using existing well known classifier algorithms. The results indicate that the methods can be significantly effective at realistic levels of noise in the training labels, but care must be taken in choosing which method to apply depending on the level of label noise.
纠正错误标记训练数据的包装方法
机器学习在疾病诊断方面有明显的应用,对于许多从大脑图像中提取的神经系统疾病特征,基于神经成像生物标志物的分类器可以为基于症状和心理测试的传统诊断方法提供有用的补充。然而,这些系统训练中使用的标签往往依赖于标准的临床诊断方法,这意味着它们在许多情况下并不完全可靠。这种不确定性使得由此引起的问题难以研究,因为很难衡量错误标签的程度及其对结果的影响。为了避免这个问题,我们根据图像进行性别分类,因为这对每个受试者都是已知的。然后,我们故意使已知的训练标签比例不正确。这使我们能够评估标签噪声水平对分类准确性的影响,并评估允许错误标记数据的方法。这些方法是使用现有的众所周知的分类器算法进行包装。结果表明,这些方法在训练标签的实际噪声水平下可以显着有效,但必须注意根据标签噪声水平选择应用哪种方法。
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