论文标题
顶部样本上二元分类的一般框架
General Framework for Binary Classification on Top Samples
论文作者
论文摘要
许多二进制分类问题最大程度地减少了上面（或以下）阈值的错误分类。我们表明，可以以这种形式编写排名问题的实例，顶部或假设测试的准确性。我们提出了一个一般框架来处理这些类别的问题，并显示了哪些已知方法（无论是已知和新提出的）属于该框架。我们提供对该框架的理论分析，并提及该方法可能遇到的选定的陷阱。我们建议一些数值改进，包括隐式衍生物和随机梯度下降。我们提供了广泛的数值研究。基于理论属性和数值实验，我们通过建议在哪种情况下使用哪种方法来结束论文。
Many binary classification problems minimize misclassification above (or below) a threshold. We show that instances of ranking problems, accuracy at the top or hypothesis testing may be written in this form. We propose a general framework to handle these classes of problems and show which known methods (both known and newly proposed) fall into this framework. We provide a theoretical analysis of this framework and mention selected possible pitfalls the methods may encounter. We suggest several numerical improvements including the implicit derivative and stochastic gradient descent. We provide an extensive numerical study. Based both on the theoretical properties and numerical experiments, we conclude the paper by suggesting which method should be used in which situation.
