Research Projects:





Sentiment and Emotion Analysis and Applications


Medical Informatics


Hierarchical Classification

We address the categorization tasks where categories are partially ordered to form a hierarchy. We introduce the notion of consistent classification which takes into account the semantics of a class hierarchy. We also propose 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, we introduce a novel hierarchical evaluation measure that has a number of 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.
Kiritchenko, S., Matwin, S., Nock, R., and Famili, F. (2006) Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization. Proceedings of the 19th Canadian Conference on Artificial Intelligence, LNCS, v. 4013, pp. 395-406, Springer, 2006 [pdf]
Kiritchenko, S. (2006) Hierarchical Text Categorization: Algorithms, Evaluation, and Applications. Ph.D. Thesis, University of Ottawa, 2006 [pdf]


Semi-Supervised Learning