A Hierarchical Tree Distance Measure for Classification
Kent Munthe Caspersen, Martin Bjeldbak Madsen, Andreas Berre Eriksen, Bo Thiesson · 2017
In this paper, we explore the problem of classification where class labels exhibit a hierarchical tree structure.Many multiclass classification algorithms assume a flat label space, where hierarchical structures are ignored.We take advantage of hierarchical structures and the interdependencies between labels.In our setting, labels are structured in a product and service hierarchy, with a focus on spend analysis.We define a novel distance measure between classes in a hierarchical label tree.This measure penalizes paths though high levels in the hierarchy.We use a known classification algorithm that aims to minimize distance between labels, given any symmetric distance measure.The approach is global in that it constructs a single classifier for an entire hierarchy by embedding hierarchical distances into a lower-dimensional space.Results show that combining our novel distance measure with the classifier induces a trade-off between accuracy and lower hierarchical distances on misclassifications.This is useful in a setting where erroneous predictions vastly change the context of a label.