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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
McCallum, A.: Multi label text classification with a mixture model trained by EM. In: AAAI 1999 Workshop on Text Learning (1999)
Joachims, T.: Text categorization with Support Vector Machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Shen, X., Boutell, M., Luo, J., Brown, C.: Multi label Machine learning and its application to semantic scene classification. In: Proceedings of the 2004 International Symposium on Electronic Imaging (EI 2004), January 18-22 (2004)
Kriegel, H.-P., Kroeger, P., Pryakhin, A., Schubert, M.: Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects. In: Proc. 4th SIAM Int. Conf. on Data Mining, pp. 102–114 (2004)
Clare, A., King, R.D.: Knowledge Discovery in Multi label Phenotype Data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001)
Blockeel, H., Bruynooghe, M., Dzeroski, S., Ramon, J., Struyf, J.: Hierarchical Multi-Classification. In: Proceedings of the First SIGKDD Workshop on Multi-Relational Data Mining (MRDM 2002), July 2002, pp. 21–35 (2002)
Wang, K., Zhou, S., Liew, S.C.: Building hierarchical classifiers using class proximity. Technical Report, National University of Singapore (1999)
The Reuters-21578, Distribution 1.0 test collection is available from, http://www.daviddlewis.com/resources/testcollections/reuters21578
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, F., Zhang, J., Honavar, V. (2005). Learning Classifiers Using Hierarchically Structured Class Taxonomies. In: Zucker, JD., Saitta, L. (eds) Abstraction, Reformulation and Approximation. SARA 2005. Lecture Notes in Computer Science(), vol 3607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527862_24
Download citation
DOI: https://doi.org/10.1007/11527862_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27872-6
Online ISBN: 978-3-540-31882-8
eBook Packages: Computer ScienceComputer Science (R0)