Detecting Ordinal Subcascades

Abstract

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.

Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts

“…Therefore, one can simply choose one of the existing divideand-conquer methods, such the error-correcting output codes (Dietterich and Bakiri, 1991), includ-ing, e.g., the one-versus-one and one-versus-all approaches. Moreover, one can also apply our proposed method in form of cascaded classification architectures (Frank and Hall, 2001), if it is possible to detect an ordinal class structure in the current classification task, as recently proposed, for instance, in (Bellmann and Schwenker, 2020;Lausser et al, 2020).…”