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 .
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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
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DOI: https://doi.org/10.1007/11550907_54
Publisher Name: Springer, Berlin, Heidelberg
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