论文标题
班级加权分类：权衡和强大的方法
Class-Weighted Classification: Trade-offs and Robust Approaches
论文作者
论文摘要
我们通过根据正确的类加权损失来解决相对于其他标签的标签可能具有较低边缘概率的问题。首先，我们检查了插件分类器的预期过量加权风险的收敛速率，在这种情况下，插件分类器的加权和风险可能有所不同。这会导致不可减少的错误，而不会融合加权贝叶斯的风险，这激发了我们考虑强大风险的考虑。我们定义了强大的风险，该风险可以最大程度地减少一组权重的风险，并在此问题上显示出多余的风险范围。最后，我们表明，加权集的特殊选择导致了从随机编程中的有条件价值（CVAR）的特殊实例，我们将其称为“风险有条件值”（LCVAR）的标签。此外，我们概括了这种权重，以得出一个新的健壮风险问题，我们称之为标签的风险异构条件值（LHCVAR）。最后，我们从经验上证明了LCVAR和LHCVAR在改善有条件风险方面的功效。
We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. First, we examine the convergence rates of the expected excess weighted risk of plug-in classifiers where the weighting for the plug-in classifier and the risk may be different. This leads to irreducible errors that do not converge to the weighted Bayes risk, which motivates our consideration of robust risks. We define a robust risk that minimizes risk over a set of weightings and show excess risk bounds for this problem. Finally, we show that particular choices of the weighting set leads to a special instance of conditional value at risk (CVaR) from stochastic programming, which we call label conditional value at risk (LCVaR). Additionally, we generalize this weighting to derive a new robust risk problem that we call label heterogeneous conditional value at risk (LHCVaR). Finally, we empirically demonstrate the efficacy of LCVaR and LHCVaR on improving class conditional risks.
