TreeSchism: Localizing Ambiguity in Hierarchies

In classification, some inputs do not support fine-grained labels. For example, a blurry photo may show that a bird is a hawk, but not which type of hawk. A classifier presented with such an input must either guess its species (possibly communicating something false) or abstain (communicating nothing). A hierarchy of classes affords a third option: name the deepest node that the image justifies. Existing hierarchical predictors either assume that ground-truth labels are leaves or can only reason about two-level taxonomies. We introduce TreeSchism, which adapts evidential deep learning to localize ambiguity to a node of an arbitrarily deep taxonomy. A TreeSchism model outputs scores at every node and accumulates them upward. This parameterization yields the refinement gap, the support for a node that does not apply to any of its descendants. We train the refinement gap to peak at the deepest label each sample warrants, and use it at inference to select a prediction granularity. Across seven taxonomies, TreeSchism matches hyper-evidential networks on the two-level taxonomies to which they are confined, improves exact-node accuracy over the strongest baseline by 6 to 21 points on deeper taxonomies, and achieves these successes without using held-out calibration data.