Biomarker selection for medical diagnosis using the partial area under the ROC curve
نویسندگان
چکیده
منابع مشابه
Regression Analysis for the Partial Area under the Roc Curve
Performance evaluation of any classification method is fundamental to its acceptance in practice. Evaluation should consider the dependence of a classifier’s accuracy on relevant covariates in addition to its overall accuracy. When developing a classifier with a continuous output that allocates units into one of two groups, receiver operating characteristic (ROC) curve analysis is appropriate. ...
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ROC analysis is increasingly being recognised as an important tool for evaluation and comparison of classifiers when the operating characteristics (i.e. class distribution and cost parameters) are not known at training time. Usually, each classifier is characterised by its estimated true and false positive rates and is represented by a single point in the ROC diagram. In this paper, we show how...
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Area Under the ROC Curve (AUC), often used for comparing classifiers, is a widely accepted performance measure for ranking instances. Many researches have studied optimization of AUC, usually via optimizing some approximation of a ranking function. Ranking SVMs are among the better performers but their usage in the literature is typically limited to learning a total ranking from partial ranking...
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ژورنال
عنوان ژورنال: BMC Research Notes
سال: 2014
ISSN: 1756-0500
DOI: 10.1186/1756-0500-7-25