Kiritchenko S., Matwin S., Nock R., Famili A.F. (2006) Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization. In: Lamontagne L., Marchand M. (eds) Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science, vol 4013. Springer, Berlin, Heidelberg
This paper deals with categorization tasks where categories are partially ordered to form a hierarchy. First, it introduces the notion of consistent classification which takes into account the semantics of a class hierarchy. Then, it presents a novel global hierarchical approach that produces consistent classification. This algorithm with AdaBoost as the underlying learning procedure significantly outperforms the corresponding “flat” approach, i.e. the approach that does not take into account the hierarchical information. In addition, the proposed algorithm surpasses the hierarchical local top-down approach on many synthetic and real tasks. For evaluation purposes, we use a novel hierarchical evaluation measure that has some attractive properties: it is simple, requires no parameter tuning, gives credit to partially correct classification and discriminates errors by both distance and depth in a class hierarchy.