Several studies point out different causes of performance degradation in supervised machine learning. Problems such as class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness limit accuracy in classification algorithms. Even though a number of approaches either in the form of a methodology or an algorithm try to minimize performance degradation, they have been isolated efforts with limited scope. Most of these approaches focus on remediation of one among many problems, with experimental results coming from few datasets and classification algorithms, insufficient measures of prediction power, and lack of statistical validation for testing the real benefit of the proposed approach. This paper consists of two main parts: In the first part, a novel probabilistic diagnostic model based on identifying signs and symptoms of each problem is presented. Thereby, early and correct diagnosis of these problems is to be achieved in order to select not only the most convenient remediation treatment but also unbiased performance metrics. Secondly, the behavior and performance of several supervised algorithms are studied when training sets have such problems. Therefore, prediction of success for treatments can be estimated across classifiers.

中文翻译：

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监督学习中退化问题的概率诊断测试

几项研究指出了监督机器学习中性能下降的不同原因。类不平衡、重叠、小分离、嘈杂标签和稀疏性等问题限制了分类算法的准确性。尽管以方法论或算法形式存在的许多方法都试图最大限度地减少性能下降，但它们是范围有限的孤立工作。这些方法中的大多数都专注于修复众多问题中的一个，实验结果来自少数数据集和分类算法，预测能力的度量不足，并且缺乏用于测试所提出方法的真正好处的统计验证。本文主要由两部分组成：第一部分，提出了一种基于识别每个问题的迹象和症状的新型概率诊断模型。因此，将实现对这些问题的早期和正确诊断，以便不仅选择最方便的修复处理，而且选择公正的性能指标。其次，当训练集存在此类问题时，研究了几种监督算法的行为和性能。因此，可以跨分类器估计治疗成功的预测。