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Abstract:
This paper presents a method for obtaining class membership
probability estimates for multiclass classification problems by
coupling the probability estimates produced by binary
classifiers. This is an extension for arbitrary code matrices of
a method due to Hastie and Tibshirani for pairwise coupling of
probability estimates. Experimental results with Boosted Naive
Bayes show that our method produces calibrated class membership
probability estimates, while having similar classification
accuracy as loss-based decoding, a method for obtaining the most
likely class that does not generate probability estimates.
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