论文标题
标签移位下的分类数据中的顺序更改点检测
Sequential changepoint detection in classification data under label shift
论文作者
论文摘要
分类器预测通常取决于以下假设：新观察结果来自与培训数据相同的分布。当基础分布发生变化时，最佳分类规则和性能可能会降低。我们考虑在依次观察，未标记的分类数据中检测出这种分布变化的问题。我们专注于标签转移对分布的变化，班级先验的转移，但班级的条件分布保持不变。我们将此问题减少到检测一维分类器分数变化的问题，从而导致简单的非参数顺序更改点检测过程。我们的程序利用分类器培训数据来估计检测统计数据，并以培训数据的规模收敛到其参数对应物。在模拟中，我们表明我们的方法在此标签移位设置中优于其他检测过程。
Classifier predictions often rely on the assumption that new observations come from the same distribution as training data. When the underlying distribution changes, so does the optimal classification rule, and performance may degrade. We consider the problem of detecting such a change in distribution in sequentially-observed, unlabeled classification data. We focus on label shift changes to the distribution, where the class priors shift but the class conditional distributions remain unchanged. We reduce this problem to the problem of detecting a change in the one-dimensional classifier scores, leading to simple nonparametric sequential changepoint detection procedures. Our procedures leverage classifier training data to estimate the detection statistic, and converge to their parametric counterparts in the size of the training data. In simulations, we show that our method outperforms other detection procedures in this label shift setting.
