Decision trees can improve accuracy by explicitly handling boundary cases through locally-computed uncertainty zones—instances near splits get soft predictions and uncertainty flags instead of hard classifications, helping downstream applications make better decisions.
This paper introduces ternary decision trees that add uncertainty zones around split thresholds, allowing predictions near decision boundaries to blend outputs from both child subtrees and flag uncertain cases.