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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Wu, F., Zhang, J., & Honavar, V. (2005). Learning classifiers using hierarchically structured class taxonomies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3607 LNAI, pp. 313–320). Springer Verlag. https://doi.org/10.1007/11527862_24
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