Consistency versus Realizable H-Consistency for Multiclass Classification


Phil Long, Rocco Servedio ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):801-809, 2013.


A consistent loss function for multiclass classification is one such that for any source of labeled examples, any tuple of scoring functions that minimizes the expected loss will have classification accuracy close to that of the Bayes optimal classifier. While consistency has been proposed as a desirable property for multiclass loss functions, we give experimental and theoretical results exhibiting a sequence of linearly separable data sources with the following property: a multiclass classification algorithm which optimizes a loss function due to Crammer and Singer (which is known not to be consistent) produces classifiers whose expected error goes to 0, while the expected error of an algorithm which optimizes a generalization of the loss function used by LogitBoost (a loss function which is known to be consistent) is bounded below by a positive constant. We identify a property of a loss function, realizable consistency with respect to a restricted class of scoring functions, that accounts for this difference. As our main technical results we show that the Crammer–Singer loss function is realizable consistent for the class of linear scoring functions, while the generalization of LogitBoost is not. Our result for LogitBoost is a special case of a more general theorem that applies to several other loss functions that have been proposed for multiclass classification.

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