AdaBoost-SAMME
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A AdaBoost-SAMME is a AdaBoost Algorithm for solving Multiclass Classification Task based on SAMME Algorithm.
- AKA: Adaboost for Sequential Additive Modeling with a Multiclass Exponential Loss Function.
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- Counter-Example(s):
- See: Sequential Additive Modeling, Exponential Loss Function, Multiclass Exponential Loss Funtion.
References
2009
- (Hastie et al., 2009) ⇒ Hastie, T., Rosset, S., Zhu, J., & Zou, H. (2009). Multi-class adaboost. Statistics and its Interface, 2(3), 349-360.
- ABSTRACT: Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducing it to multiple two-class problems. We show that the proposed multi-class AdaBoost algorithm is equivalent to a forward stagewise additive modeling algorithm that minimizes a novel exponential loss for multi-class classification. Furthermore, we show that the exponential loss is a member of a class of Fisher-consistent loss functions for multi-class classification. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive in terms of misclassification error rate.