A Multi-class Boosting Method with Direct Optimization

author: Shaodan Zhai, Wright State University
published: Oct. 7, 2014,   recorded: August 2014,   views: 1741

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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.

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