Improvement of Boosting Algorithm by Modifying the Weighting Rule
Improvement of Boosting Algorithm by Modifying the Weighting Rule
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DOI：
10.1023/b:amai.0000018577.32783.d2
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发表时间：
2004-05
影响因子：
1.2
通讯作者：
Masayuki Nakamura;Hiroki Nomiya;K. Uehara
中科院分区：
文献类型：
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作者：
Masayuki Nakamura;Hiroki Nomiya;K. Uehara
AdaBoost是一种通过组合由学习算法创建的假设来提高给定学习算法的分类精度的方法。AdaBoost的一个缺点是，当训练样本包括噪声样本或异常样本（称为硬样本）时，它的性能会下降。这一现象导致AdaBoost对困难样本赋予过高的权重。在本研究中，我们将阈值引入AdaBoost的权重规则中，以防止权重被赋予过高的值。在学习过程中，我们比较了我们的方法与AdaBoost的分类错误的上限，我们设置阈值，使我们的方法的上限可以上级AdaBoost的。我们的方法表现出比AdaBoost更好的性能。
AdaBoost is a method for improving the classification accuracy of a given learning algorithm by combining hypotheses created by the learning alogorithms. One of the drawbacks of AdaBoost is that it worsens its performance when training examples include noisy examples or exceptional examples, which are called hard examples. The phenomenon causes that AdaBoost assigns too high weights to hard examples. In this research, we introduce the thresholds into the weighting rule of AdaBoost in order to prevent weights from being assigned too high value. During learning process, we compare the upper bound of the classification error of our method with that of AdaBoost, and we set the thresholds such that the upper bound of our method can be superior to that of AdaBoost. Our method shows better performance than AdaBoost.