A Theory of Multiclass Boosting

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Indraneel Mukherjee, Robert E. Schapire


Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.