论文标题
多项式分类的可分解关节变化
Factorizable Joint Shift in Multinomial Classification
论文作者
论文摘要
最近提出了可取分的关节移位（FJS）作为一种数据集偏移，可以通过一种称为关节重要性对准的方法从测试数据集上的特征数据观察结果估算完整特征。对于多项式（多类）分类设置，我们在源（训练）分布，目标（测试）的类别概率和特征的目标边缘分布方面得出了可分解的关节变化的表示。在此结果的基础上，我们提出了联合重要性对准的替代方案，同时指出，如果没有可用的类标签信息，并且没有做出其他假设，则不能完全识别可分解的关节移位。本文的其他结果包括在一般数据集偏移和可分解的关节移位下的后类概率的校正公式。此外，我们研究了假设可分解的关节变化对样品选择引起的偏差的后果。
Factorizable joint shift (FJS) was recently proposed as a type of dataset shift for which the complete characteristics can be estimated from feature data observations on the test dataset by a method called Joint Importance Aligning. For the multinomial (multiclass) classification setting, we derive a representation of factorizable joint shift in terms of the source (training) distribution, the target (test) prior class probabilities and the target marginal distribution of the features. On the basis of this result, we propose alternatives to joint importance aligning and, at the same time, point out that factorizable joint shift is not fully identifiable if no class label information on the test dataset is available and no additional assumptions are made. Other results of the paper include correction formulae for the posterior class probabilities both under general dataset shift and factorizable joint shift. In addition, we investigate the consequences of assuming factorizable joint shift for the bias caused by sample selection.
