Consistency Analysis for Binary Classification Revisited
Consistency Analysis for Binary Classification Revisited
复制标题
重新审视二元分类的一致性分析
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Nagarajan Natarajan
中科院分区：
文献类型：
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作者：
K. Dembczynski;W. Kotłowski;Oluwasanmi Koyejo;Nagarajan Natarajan
统计学习理论正处于一个拐点，这得益于在理解和优化众多指标方面的最新进展。特别令人感兴趣的是非可分解指标，例如F值和杰卡德系数，它们不能表示为对样本的简单平均。不可分解性是理论分析困难的主要根源，有趣的是，它导致了两种不同的情形以及一致性的概念。在本文中，我们从统计学和算法的角度分析这两种情形，以探索它们之间的联系，并针对多种指标强调它们之间的差异。该分析补充了此前关于这一主题的研究结果，阐明了围绕这两种情形的常见混淆之处，并为使用复杂指标进行二元分类的理论和实践提供了指导。
Statistical learning theory is at an inflection point enabled by recent advances in understanding and optimizing a wide range of metrics. Of particular interest are non-decomposable metrics such as the F-measure and the Jaccard measure which cannot be represented as a simple average over examples. Non-decomposability is the primary source of difficulty in theoretical analysis, and interestingly has led to two distinct settings and notions of consistency. In this manuscript we analyze both settings, from statistical and algorithmic points of view, to explore the connections and to highlight differences between them for a wide range of metrics. The analysis complements previous results on this topic, clarifies common confusions around both settings, and provides guidance to the theory and practice of binary classification with complex metrics.
影响因子：
7.5
作者：
Dembczynski, Krzysztof;Waegeman, Willem;Huellermeier, Eyke
通讯作者：
Huellermeier, Eyke