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Neyman-Pearson Classification with Indecisions: Simultaneous Control of Selective Type I and Type II Errors
Binary classification often requires controlling both type I and type II error rates at prespecified levels. When the class distributions overlap substantially, the two error limits may be incompatible if every observation must be classified. We study Neyman-Pearson classification with indecisions (
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