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Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios

作者: Jan Mielniczuk,Adam Wawrzeńczyk

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In the paper we argue that performance of the classifiers based on Empirical Risk Minimization (ERM) for positive unlabeled data, which are designed for case-control sampling scheme may significantly deteriorate when applied to a single-sample scenario. We reveal why their behavior depends, in all but very specific cases, on the scenario. Also, we introduce a single-sample case analogue of the popular non-negative risk classifier designed for case-control data and compare its performance with the original proposal. We show that the significant differences occur between them, especiall when half or more positive of observations are labeled. The opposite case when ERM minimizer designed for the case-control case is applied for single-sample data is also considered and similar conclusions are drawn. Taking into account difference of scenarios requires a sole, but crucial, change in the definition of the Empirical Risk.

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本作品采用 知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可,转载请附上原文出处链接和本声明。
本文链接地址:https://www.flyai.com/paper_detail/83184
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