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Correlation analysis of performance measures for multi-label classification

机译：多标签分类绩效指标的相关性分析

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摘要

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. In order to evaluate and compare multi-label classifiers, researchers have adapted evaluation measures from the single-label paradigm, like Precision and Recall; and also have developed many different measures specifically for the multi-label paradigm, like Hamming Loss and Subset Accuracy. However, these evaluation measures have been used arbitrarily in multi-label classification experiments, without an objective analysis of correlation or bias. This can lead to misleading conclusions, as the experimental results may appear to favor a specific behavior depending on the subset of measures chosen. Also, as different papers in the area currently employ distinct subsets of measures, it is difficult to compare results across papers. In this work, we provide a thorough analysis of multi-label evaluation measures, and we give concrete suggestions for researchers to make an informed decision when choosing evaluation measures for multi-label classification.
机译：在许多重要的应用领域中，例如文本分类，场景分类，生物分子分析和医学诊断，示例自然与多个类别标签相关联，从而引起了多标签分类问题。近年来，这一事实导致了对多标签分类的大量研究。为了评估和比较多标签分类器，研究人员从单标签范例中采用了评估方法，例如Precision和Recall；并且还针对多标签范例开发了许多不同的度量，例如汉明损失和子集精度。但是，这些评估方法已在多标签分类实验中随意使用，而没有客观分析相关性或偏差。这可能会导致误导性的结论，因为根据选择的测量方法的子集，实验结果似乎倾向于特定行为。此外，由于该领域的不同论文目前采用不同的度量子集，因此很难比较论文之间的结果。在这项工作中，我们对多标签评估措施进行了全面分析，并为研究人员在选择多标签分类评估措施时做出明智的决策提供了具体建议。

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