基于弱分类器调整的多分类Adaboost算法

杨新武; 马壮; 袁顺

doi:10.11999/JEIT150544

基于弱分类器调整的多分类Adaboost算法

doi: 10.11999/JEIT150544

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出版历程
- 收稿日期: 2015-05-11
- 修回日期: 2015-10-08
- 刊出日期: 2016-02-19

Multi-class Adaboost Algorithm Based on the Adjusted Weak Classifier

摘要

摘要: Adaboost.M1算法要求每个弱分类器的正确率大于1/2，但在多分类问题中寻找这样的弱分类器较为困难。有学者提出了多类指数损失函数的逐步添加模型(SAMME)，把弱分类器的正确率要求降低到大于1/k(k为类别数)，降低了寻找弱分类器的难度。由于SAMME算法无法保证弱分类器的有效性，从而并不能保证最终强分类器正确率的提升。为此，该文通过图示法及数学方法分析了多分类Adaboost算法的原理，进而提出一种新的既可以降低弱分类器的要求，又可以确保弱分类器有效性的多分类方法。在UCI数据集上的对比实验表明，该文提出的算法的结果要好于SAMME算法，并达到了不弱于Adaboost.M1算法的效果。
- 多类分类器 /
- 多类指数损失函数的逐步添加模型 /
- Adaboost.M1
Abstract: Adaboost.M1 requires each weak classifier,s accuracy rate more than 1/2. But it is difficult to find a weak classifier which accuracy rate more than 1/2 in a multiple classification issues. Some scholars put forward the Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME) algorithm, it reduces the weak classifier accuracy requirements, from more than 1/2 to more than 1/k (k is the category number). SAMME algorithm reduces the difficulty to find weak classifier. But, due to the SAMME algorithm is no guarantee that the effectiveness of the weak classifier, which does not ensure that the final classifier improves classification accuracy. This paper analyzes the multi-class Adaboost algorithm by graphic method and math method, and then a new kind of multi-class classification method is proposed which not only reduces the weak classifier accuracy requirements, but also ensures the effectiveness of the weak classifier. In the benchmark experiments on UCI data sets show that the proposed algorithm are better than the SAMME, and achieves the effect of Adaboost.M1.
- Multi-class classification /
- Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME) /
- Adaboost.M1

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