Multi-class boosting with asymmetric binary weak-learners

作者：Fernandez Baldera Antonio; Baumela Luis^*

来源：Pattern Recognition, 2014, 47(5): 2080-2090.

DOI：10.1016/j.patcog.2013.11.024

• AdaBoost
• Multi-class classification
• Asymmetric binary weak-learners
• Class imbalance

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.

• 出版日期2014-5