Multi-class boosting with asymmetric binary weak-learners

Authors:
Antonio Fernández-Baldera;Luis Baumela
Affiliations:
-;-
Venue:
Pattern Recognition
Year:
2014

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Abstract

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, GentleBoost and the SAMME algorithms.