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Multi-class boosting with asymmetric binary weak-learners
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DOI：10.1016/j.patcog.2013.11.024.png)
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We introduce a multi-class generalization of AdaBoost with binary weak-learners. We use a vectorial codification to represent class labels and a multi-class exponential loss function to evaluate classifier responses. This representation produces a set of margin values that provide a range of punishments for failures and rewards for successes. Moreover, the stage-wise optimization of this model introduces an asymmetric boosting procedure whose costs depend on the number of classes separated by each weak-learner. In this way the boosting algorithm takes into account class imbalances when building the ensemble. The experiments performed compare this new approach favorably to AdaBoost.MH, Gentle-Boost and the SAMME algorithms. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
AdaBoost
Multi-class classification
Asymmetric binary weak-learners
Class imbalance
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