Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization

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TypeArticle
ConferenceProceedings of the 19th Canadian Conference on Artificial Intelligence, June 7-9, 2006., Québec City, Québec, Canada
AbstractThis 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48737
NPARC number5763573
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Record identifier290b8fca-ac33-4d67-ae83-8b68e53bc240
Record created2009-03-29
Record modified2016-05-09
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