Abstract
We propose a novel probabilistic model for constructing a multi-class pattern classifier by weighted aggregation of general binary classifiers including one-versus-the-rest, one-versus-one, and others. Our model has a latent variable that represents class membership probabilities, and it is estimated by fitting it to probability estimate outputs of binary classfiers. We apply our method to classification problems of synthetic datasets and a real world dataset of gene expression profiles. We show that our method achieves comparable performance to conventional voting heuristics.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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.
Similar content being viewed by others
References
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, Hoboken (2000)
Oba, S., Sato, M., Ishii, S.: Variational Bayes method for Mixture of Principal Component Analyzers. In: Proceeding for 7th International Conference on Neural Information Processing (ICONIP 2000), vol. 2, pp. 1416–1421 (2000)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Schölkopf, B., Burges, C., Vapnik, V.: Extracting support data for a given task. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pp. 252–257 (1995)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems, vol. 10. The MIT Press, Cambridge (1998)
Schölkopf, B., Burges, C., Smola, A.: Advances in Kernel Methods Support Vector Learning. The MIT Press, Cambridge (1999)
Li, T., Zhang, C., Ogihara, M.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20, 2429–2437 (2004)
Anderson, J.: Logistic discrimination. Biometrika 59, 19–35 (1972)
Bradley, R.A., Terry, M.E.: Rank Analysis of incomplete block designs, I. The method of paired comparisons. Biometrika 41, 324–345 (1952)
Tax, D., Duin, R.P.W.: Using two-class classifiers for multi-class classification. In: Proceedings 16th International Conference on Pattern Recognition (ICPR). vol. 2, pp. 124–127 (2002)
Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98, 15149–15154 (2001)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
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
Yukinawa, N., Oba, S., Kato, K., Ishii, S. (2005). Multi-class Pattern Classification Based on a Probabilistic Model of Combining Binary Classifiers. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_54
Download citation
DOI: https://doi.org/10.1007/11550907_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)