Learning classifiers using hierarchically structured class taxonomies

Authors:
Feihong Wu;Jun Zhang;Vasant Honavar
Affiliations:
Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, Iowa;Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, Iowa;Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, Iowa
Venue:
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Year:
2005

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Abstract

We consider classification problems in which the class labels are organized into an abstraction hierarchy in the form of a class taxonomy. We define a structured label classification problem. We explore two approaches for learning classifiers in such a setting. We also develop a class of performance measures for evaluating the resulting classifiers. We present preliminary results that demonstrate the promise of the proposed approaches.