Abstract
We present a novel methods for multi-class classification by ensemble of binary classifiers for multi-class classification. The proposed method is characterized by a minimization problem of weighted divergences, and includes a lot of conventional methods as special cases. We discuss relationship between the proposed method and conventional methods and statistical properties of the proposed method. A small experiment shows that the proposed method can effectively incorporate information of multiple binary classifiers into multi-class classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2001)
Amari, S., Nagaoka, H.: Methods of Information Geometry. Translations of Mathematical Monographs, vol. 191. Oxford University Press (2000)
Crammer, K., Singer, Y.: On the learnability and design of output codes for multiclass problems. Machine Learning 47(2), 201–233 (2002)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Genest, C., Zidek, J.V.: Combining probability distributions: A critique and an annotated bibliography. Statistical Science 1(1), 114–135 (1986)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Annals of Statistics 26, 451–471 (1998)
Murata, N., Takenouchi, T., Kanamori, T., Eguchi, S.: Information geometry of U-boost and bregman divergence. Neural Computation 16(7), 1437–1481 (2004)
Takenouchi, T., Ishii, S.: A multi-class classification method based on decoding of binary classifiers. Neural Computation 21(7), 2049–2081 (2009)
Takenouchi, T., Ishii, S.: Ternary bradley-terry model-based decoding for multi-class classification and its extensions. Machine Learning, 1–24 (2011)
Weston, J., Watkins, C.: Support vector machines for multi-class pattern recognition. In: Proceedings of the Seventh European Symposium on Artificial Neural Networks, vol. 4(6) (1999)
Zadrozny, B.: Reducing multiclass to binary by coupling probability estimates. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takenouchi, T., Ishii, S. (2012). A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-34481-7_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34480-0
Online ISBN: 978-3-642-34481-7
eBook Packages: Computer ScienceComputer Science (R0)