Comparing classifiers when the misallocation costs are uncertain

被引:130
作者
Adams, NM [1 ]
Hand, DJ [1 ]
机构
[1] Open Univ, Dept Stat, Milton Keynes MK7 6AA, Bucks, England
基金
英国工程与自然科学研究理事会;
关键词
ROC curve; error rate; loss function; misclassification costs; classification rule; supervised classification;
D O I
10.1016/S0031-3203(98)00154-X
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Receiver Operating Characteristic (ROC) curves are popular ways of summarising the performance of two class classification rules. In fact, however, they are extremely inconvenient. If the relative severity of the two different kinds of misclassification is known, then an awkward projection operation is required to deduce the overall loss. At the other extreme, when the relative severity is unknown, the area. under an ROC curve is often used as an index of performance. However, this essentially assumes that nothing whatsoever is known about the relative severity - a situation which is very rare in real problems. We present an alternative plot which is more revealing than an ROC plot and we describe a comparative index which allows one to take advantage of anything that may be known about the relative severity of the two kinds of misclassification. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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页码:1139 / 1147
页数:9
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