An empirical comparison of ideal and empirical ROC-Based reject rules

被引：0
|
作者：
Marrocco, Claudio [1 ]
Molinara, Mario [1 ]
Tortorella, Francesco [1 ]
机构：
[1] Univ Cassino, DAEIMI, Via G Di Biasio 43, I-03043 Cassino, Italy
来源：
MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION, PROCEEDINGS | 2007年 / 4571卷
关键词：
ROC curve; reject option; two-class classification; cost-sensitive classification; decision theory;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Two class classifiers are used in many complex problems in which the classification results could have serious consequences. In such situations the cost for a wrong classification can be so high that can be convenient to avoid a decision and reject the sample. This paper presents a comparison between two different reject rules (the Chow's and the ROC rule). In particular, the experiments show that the Chow's rule is inappropriate when the estimates of the a posteriori probabilities are not reliable.
引用
收藏
页码：47 / +
页数：2
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