Learning Classifiers Using Hierarchically Structured Class Taxonomies

  • Feihong Wu
  • Jun Zhang
  • Vasant Honavar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3607)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Feihong Wu
    • 1
  • Jun Zhang
    • 1
  • Vasant Honavar
    • 1
  1. 1.Artificial Intelligence Research Laboratory, Department of Computer ScienceIowa State UniversityAmesUSA

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