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基于弱分類器調(diào)整的多分類Adaboost算法

楊新武,  馬壯,  袁順

楊新武, 馬壯, 袁順. 基于弱分類器調(diào)整的多分類Adaboost算法[J]. 電子與信息學(xué)報, 2016, 38(2): 373-380. doi: 10.11999/JEIT150544
引用本文: 楊新武, 馬壯, 袁順. 基于弱分類器調(diào)整的多分類Adaboost算法[J]. 電子與信息學(xué)報, 2016, 38(2): 373-380. doi: 10.11999/JEIT150544
YANG Xinwu, MA Zhuang, YUAN Shun. Multi-class Adaboost Algorithm Based on the Adjusted Weak Classifier[J]. Journal of Electronics & Information Technology, 2016, 38(2): 373-380. doi: 10.11999/JEIT150544
Citation: YANG Xinwu, MA Zhuang, YUAN Shun. Multi-class Adaboost Algorithm Based on the Adjusted Weak Classifier[J]. Journal of Electronics & Information Technology, 2016, 38(2): 373-380. doi: 10.11999/JEIT150544

基于弱分類器調(diào)整的多分類Adaboost算法

doi: 10.11999/JEIT150544

Multi-class Adaboost Algorithm Based on the Adjusted Weak Classifier

  • 摘要: Adaboost.M1算法要求每個弱分類器的正確率大于1/2，但在多分類問題中尋找這樣的弱分類器較為困難。有學(xué)者提出了多類指數(shù)損失函數(shù)的逐步添加模型(SAMME)，把弱分類器的正確率要求降低到大于1/k(k為類別數(shù))，降低了尋找弱分類器的難度。由于SAMME算法無法保證弱分類器的有效性，從而并不能保證最終強分類器正確率的提升。為此，該文通過圖示法及數(shù)學(xué)方法分析了多分類Adaboost算法的原理，進而提出一種新的既可以降低弱分類器的要求，又可以確保弱分類器有效性的多分類方法。在UCI數(shù)據(jù)集上的對比實驗表明，該文提出的算法的結(jié)果要好于SAMME算法，并達到了不弱于Adaboost.M1算法的效果。
    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.
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出版歷程
  • 收稿日期:  2015-05-11
  • 修回日期:  2015-10-08
  • 刊出日期:  2016-02-19

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