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Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework
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DOI：10.1016/j.patrec.2006.11.001.png)
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Abstract
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Multi-class AdaBoost algorithms AdaBooost.MO, -ECC and -OC have received a great attention in the literature, but their relationships have not been fully examined to date. In this paper, we present a novel interpretation of the three algorithms, by showing that MO and ECC perform stage-wise functional gradient descent on a cost function defined over margin values, and that OC is a shrinkage version of ECC. This allows us to strictly explain the properties of ECC and OC, empirically observed in prior work. Also, the outlined interpretation leads us to introduce shrinkage as regularization in MO and ECC, and thus to derive two new algorithms: SMO and SECC. Experiments on diverse databases are performed. The results demonstrate the effectiveness of the proposed algorithms and validate our theoretical findings. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
AdaBoost
margin theory
multi-class classification problem
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