Abstract
In this paper we present methods of enhancing existing discriminative classifiers for multi-labeled predictions. Discriminative methods like support vector machines perform very well for uni-labeled text classification tasks. Multi-labeled classification is a harder task subject to relatively less attention. In the multi-labeled setting, classes are often related to each other or part of a is-a hierarchy. We present a new technique for combining text features and features indicating relationships between classes, which can be used with any discriminative algorithm. We also present two enhancements to the margin of SVMs for building better models in the presence of overlapping classes. We present results of experiments on real world text benchmark datasets. Our new methods beat accuracy of existing methods with statistically significant improvements.
Keywords
- Support Vector Machine
- Document Vector
- Discriminative Method
- Label Dimension
- Patent Dataset
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.
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
References
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, Springer, Heidelberg (1998)
Schapire, R.E., Singer, Y.: Boostexter: A boosting-based system for text categorization. Machine Learning 39(2/3), 135–168 (2000)
Vapnik, V.: Statistical Learning Theory. John Wiley, Chichester (1998)
Sarawagi, S., Chakrabarti, S., Godbole, S.: Cross training: learning probabilistic mappings between topics. In: Proceedings of the ACM SIGKDD 2003 (2003)
Godbole, S., Sarawagi, S., Chakrabarti, S.: Scaling multi-class support vector machines using inter-class confusion. In: Proceedings of ACM SIGKDD 2002 (2002)
Crammer, K., Singer, Y.: A family of additive online algorithms for category ranking. Journal of Machine Learning Research, 1025–1058 (2003)
Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classification and categorical regression problems. Technical Report, BioWulf Technologies (2001)
Yu, H., Han, J., Pebl, K.C.-C.: Positive example-based learning for web page classification using SVM. In: Proceedings of ACM SIGKDD 2002 (2002)
McCallum, A.: Multi-label text classification with a mixture model trained by EM. In: AAAI Workshop on Text Learning 1999 (1999)
Hofmann, T., Puzicha, J.: Unsupervised learning from dyadic data. Technical Report TR-98-042, Berkeley (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Godbole, S., Sarawagi, S. (2004). Discriminative Methods for Multi-labeled Classification. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-24775-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
Online ISBN: 978-3-540-24775-3
eBook Packages: Springer Book Archive