  Hierarchies are frequently used for the organization of objects. Given a
hierarchy of classes, two main approaches are used, to automatically classify
new instances: flat classification and cascade classification. Flat
classification ignores the hierarchy, while cascade classification greedily
traverses the hierarchy from the root to the predicted leaf. In this paper we
propose a new approach, which extends cascade classification to predict the
right leaf by estimating the probability of each root-to-leaf path. We provide
experimental results which indicate that, using the same classification
algorithm, one can achieve better results with our approach, compared to the
traditional flat and cascade classifications.
