论文标题
成本敏感大幅度分类器的分配保证金方法
Apportioned Margin Approach for Cost Sensitive Large Margin Classifiers
论文作者
论文摘要
我们考虑成本敏感的多类分类的问题，我们希望以不太重要的阶层为代价提高重要类的敏感性。我们采用一个{\ em分配的保证金}框架来解决此问题，这使共享相同边界的类之间有效的边距转移。所有一对班级之间的决策边界根据给定的优先矢量将它们之间的边缘划分为它们之间的边缘，这会导致重要类绑定的更严格的误差，同时还减少了整体样本外误差。除了证明我们的框架的有效实施外，我们还得出了概括界限，展示了Fisher的一致性，将框架适应Mercer的内核和神经网络，并在所有帐户上报告有希望的经验结果。
We consider the problem of cost sensitive multiclass classification, where we would like to increase the sensitivity of an important class at the expense of a less important one. We adopt an {\em apportioned margin} framework to address this problem, which enables an efficient margin shift between classes that share the same boundary. The decision boundary between all pairs of classes divides the margin between them in accordance to a given prioritization vector, which yields a tighter error bound for the important classes while also reducing the overall out-of-sample error. In addition to demonstrating an efficient implementation of our framework, we derive generalization bounds, demonstrate Fisher consistency, adapt the framework to Mercer's kernel and to neural networks, and report promising empirical results on all accounts.
