A multi-class boosting method with direct optimization
Shaodan Zhai, Tian Xia, Shaojun Wang · 2014
We present a direct multi-class boosting (DMCBoost) method for classification with the following properties: (i) instead of reducing the multi-class classification task to a set of binary classification tasks, DMCBoost directly solves the multi-class classification problem, and only requires very weak base classifiers; (ii) DMCBoost builds an ensemble classifier by directly optimizing the non-convex performance measures, including the empirical classification error and margin functions, without resorting to any upper bounds or approximations. As a non-convex optimization method, DMCBoost shows competitive or better results than state-of-the-art convex relaxation boosting methods, and it performs especially well on the noisy cases.