Multi-class boosting with asymmetric binary weak-learners

• Multi-class boosting procedure with binary weak-learners.
• Novel multi-class vectorial encoding producing different margin values.
• Margins depend on the asymmetry of the problem posed to the weak-learner.
• Provides statistically significant improvements in performance.
• Opens research venues in multi-{class,label,dimensional} classification.

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.