论文标题
Xtreme边距：二进制分类问题的可调损失函数
Xtreme Margin: A Tunable Loss Function for Binary Classification Problems
论文作者
论文摘要
损失功能推动了机器学习算法的优化。损失功能的选择可以对模型的训练以及模型如何学习数据产生重大影响。二进制分类是机器学习问题的主要支柱之一，用于医学成像用于故障检测应用。最常用的二进制分类替代损失函数包括二进制跨透镜和铰链损失函数，这构成了我们研究的重点。 在本文中，我们提供了新型损耗函数的概述，即Xtreme rabin损失函数。与二进制跨渗透性和铰链损失功能不同，此损失功能为研究人员和从业人员提供了训练过程的灵活性，从最大化精度和AUC得分到最大程度地提高特定类别的条件准确性，通过可调的超级标准$λ_1$和$λ_1$和$λ_2$，即改变其训练的价值，将改变其模型的训练。
Loss functions drive the optimization of machine learning algorithms. The choice of a loss function can have a significant impact on the training of a model, and how the model learns the data. Binary classification is one of the major pillars of machine learning problems, used in medical imaging to failure detection applications. The most commonly used surrogate loss functions for binary classification include the binary cross-entropy and the hinge loss functions, which form the focus of our study. In this paper, we provide an overview of a novel loss function, the Xtreme Margin loss function. Unlike the binary cross-entropy and the hinge loss functions, this loss function provides researchers and practitioners flexibility with their training process, from maximizing precision and AUC score to maximizing conditional accuracy for a particular class, through tunable hyperparameters $λ_1$ and $λ_2$, i.e., changing their values will alter the training of a model.
