期刊
PATTERN RECOGNITION LETTERS
卷 27, 期 8, 页码 882-891出版社
ELSEVIER
DOI: 10.1016/j.patrec.2005.10.012
关键词
ROC analysis; cost-sensitive learning; classifier evaluation
Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs have been used in cost-sensitive learning because of the ease with which class skew and error cost information can be applied to them to yield cost-sensitive decisions. However, they have been criticized because of their inability to handle instance-varying costs; that is, domains in which error costs vary from one instance to another. This paper presents and investigates a technique for adapting ROC graphs for use with domains in which misclassification costs vary within the instance population. (c) 2005 Elsevier B.V. All rights reserved.
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