Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation
Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation
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DOI：
10.1007/11941439_114
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发表时间：
2006-12
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影响因子：
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通讯作者：
Marina Sokolova;N. Japkowicz;S. Szpakowicz
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文献类型：
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作者：
Marina Sokolova;N. Japkowicz;S. Szpakowicz
不同的评估方法评估机器学习算法的不同特征。算法和分类器的经验评估是研究人员之间持续争论的问题。目前使用的大多数度量都集中在分类器正确识别类的能力上。我们注意到其他有用的属性，如失败避免或阶级歧视，并提出了评估这些属性的措施。这些度量——约登指数、可能性、判别力——用于医学诊断。我们表明它们是相互关联的，并将它们应用于电子谈判领域的案例研究。我们还列出了其他可能从这些措施的应用中受益的学习问题。
Different evaluation measures assess different characteristics of machine learning algorithms. The empirical evaluation of algorithms and classifiers is a matter of on-going debate among researchers. Most measures in use today focus on a classifier’s ability to identify classes correctly. We note other useful properties, such as failure avoidance or class discrimination, and we suggest measures to evaluate such properties. These measures – Youden’s index, likelihood, Discriminant power – are used in medical diagnosis. We show that they are interrelated, and we apply them to a case study from the field of electronic negotiations. We also list other learning problems which may benefit from the application of these measures.