A systematic analysis of performance measures for classification tasks
A systematic analysis of performance measures for classification tasks
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DOI：
10.1016/j.ipm.2009.03.002
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发表时间：
2009-07-01
影响因子：
8.6
通讯作者：
Lapalme, Guy
中科院分区：
文献类型：
--
作者：
Sokolova, Marina;Lapalme, Guy
本文系统地分析了机器学习分类任务中使用的24个性能指标，即，二进制、多类、多标记和分层。对于每个分类任务，该研究将混淆矩阵中的一组变化与数据的特定特征相关联。然后，分析集中在混淆矩阵的变化类型上，这些变化不会改变度量，因此，保持分类器的评估（度量不变性）。结果是关于分类问题中所有相关标签分布变化的度量不变性分类法。这种形式化的分析支持的应用程序的例子中，不变性的措施导致一个更可靠的评估分类。文本分类用几个案例研究补充讨论。(C)2009爱思唯尔有限公司保留所有权利。
This paper presents a systematic analysis of twenty four performance measures used in the complete spectrum of Machine Learning classification tasks, i.e., binary, multi-class, multi-labelled, and hierarchical. For each classification task, the study relates a set of changes in a confusion matrix to specific characteristics of data. Then the analysis concentrates on the type of changes to a confusion matrix that do not change a measure, therefore, preserve a classifier's evaluation (measure invariance). The result is the Measure invariance taxonomy with respect to all relevant label distribution changes in a classification problem. This formal analysis is supported by examples of applications where invariance properties of measures lead to a more reliable evaluation of classifiers. Text classification Supplements the discussion with several case studies. (C) 2009 Elsevier Ltd. All rights reserved.