典型文献
Maximum Net Benefit Indicator and Its Applications
文献摘要：
Receiver operating characteristics(ROC)curve and the area under the curve(AUC)value are often used to illustrate the diagnostic ability of binary classifiers.However,both ROC and AUC focus on high accuracy in theory,which may not be effective for practical applications.In addition,it is difficult to judge which one is better when the ROC curves are intersect and the AUC values are equal.Decision curve analysis(DCA)methods improve ROC by incorporating accuracy and consequences.However,similar to ROC,DCA requires a quantitative indicator to objectively determine which one is better when DCA curves intersect.A DCA-based statistical indicator named maximum net benefit(MNB)is constructed for evaluating clinical treatment regimens rather than just accuracy as in ROC and AUC.As a simple and effective statistical indicator,the construction process of MNB is given theoretically.Moreover,the MNB can still provide effective identification when the AUC values are equal,which is proved by theory.Furthermore,the feasibility and effectiveness of the proposed MNB are verified by gene selection and classifier performance comparison on actual data.
文献关键词：
中图分类号：
作者姓名：
YANG Xiao-hui;BAI Xin-yu;LI Zi-xin;HUANG Kun
作者机构：
Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms,School of Mathematics and Statistics,Henan University,Kaifeng 475004,China;Department of Medicine,Indiana University School of Medicine,Indianapolis 46202,USA
文献出处：
引用格式：
[1]YANG Xiao-hui;BAI Xin-yu;LI Zi-xin;HUANG Kun-.Maximum Net Benefit Indicator and Its Applications)[J].数学季刊（英文版）,2022(03):248-259
A类：
B类：
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AB值：
0.563346
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