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A probabilistic decoding approach to multi-class classification

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2 Author(s)
Takenouchi, T. ; Nara Inst. of Sci. & Technol., Nara ; Ishii, S.

In this article, we propose a new method of multi-class classification in the framework of error-correcting output coding (ECOC). Misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model for each class and dependence between binary classifiers is incorporated into our model, which makes a decoder, a type of Boltzmann machine. Experimental studies using a synthetic dataset and datasets from UCI repository are performed, and the results show that the proposed method is superior to other existing multi-class classification methods.

Published in:

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Date of Conference:

12-17 Aug. 2007

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