swMATH ID: 19134
Software Authors: Zhu, Ji; Zou, Hui; Rosset, Saharon; Hastie, Trevor
Description: Multi-class AdaBoost. 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. 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 this paper, the new algorithm is extremely easy to implement and is highly competitive in terms of misclassification error rate.
Homepage: http://www.intlpress.com/site/pub/files/_fulltext/journals/sii/2009/0002/0003/SII-2009-0002-0003-a008.pdf
Dependencies: R
Related Software: AdaBoost.MH; UCI-ml; ElemStatLearn; rpart; XGBoost; StatLib Datasets Archive; R; adabag; mlbench; Scikit; glmnet; LIBSVM; SIFT; gbm; caret; CRAN Task Views; ada; mboost; digeR; gss
Cited in: 24 Publications

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