Chapter

Abstraction, Reformulation and Approximation

Volume 3607 of the series Lecture Notes in Computer Science pp 313-320

Learning Classifiers Using Hierarchically Structured Class Taxonomies

  • Feihong WuAffiliated withArtificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University
  • , Jun ZhangAffiliated withArtificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University
  • , Vasant HonavarAffiliated withArtificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University

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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.