Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.

有序分类器级联受数据集语义类标签的假设顺序约束。此顺序确定特征空间中决策区域的整体结构。假设这些类标签上的顺序正确，将具有较高的泛化性能，而错误的顺序将导致结果降低。这样，序数分类器系统可以促进探索性数据分析，从而允许筛选分类标签的潜在候选顺序。以前，我们已经表明可以对所有类别标签的总订单进行筛选。但是，由于数据集可能包含有序类和非有序类的样本，因此总排序的假设可能不合适。需要对类的子集进行分析以检测此类隐藏的序数子结构。在这项工作中 我们通过限制我们必须检查的枚举数，设计了一种新颖的筛选程序，用于详尽评估类的所有子集的所有顺序排列。对来自不同应用程序的多类数据进行的实验表明，序数子结构会生成新的并支持已知关系。