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Analysis of Generalization Ability for Different AdaBoost Variants Based on Classification and Regression Trees

机译：基于分类和回归树的不同AdaBoost变体的泛化能力分析

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摘要

As a machine learning method, AdaBoost is widely applied to data classification and object detection because of its robustness and efficiency. AdaBoost constructs a global and optimal combination of weak classifiers based on a sample reweighting. It is known that this kind of combination improves the classification performance tremendously. As the popularity of AdaBoost increases, many variants have been proposed to improve the performance of AdaBoost. Then, a lot of comparison and review studies for AdaBoost variants have also been published. Some researchers compared different AdaBoost variants by experiments in their own fields, and others reviewed various AdaBoost variants by basically introducing these algorithms. However, there is a lack of mathematical analysis of the generalization abilities for different AdaBoost variants. In this paper, we analyze the generalization abilities of six AdaBoost variants in terms of classification margins. The six compared variants are Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, Parameterized AdaBoost, Margin-pruning Boost, and Penalized AdaBoost. Finally, we use experiments to verify our analyses.
机译：作为一种机器学习方法，AdaBoost的鲁棒性和效率使其广泛应用于数据分类和对象检测。 AdaBoost基于样本权重构建全局和弱分类器的最佳组合。众所周知，这种组合极大地提高了分类性能。随着AdaBoost的流行，提出了许多改进AdaBoost性能的变体。然后，关于AdaBoost变体的许多比较和综述研究也已经发表。一些研究人员通过各自领域的实验比较了不同的AdaBoost变体，另一些研究人员通过基本介绍这些算法来审查了各种AdaBoost变体。但是，缺乏对不同AdaBoost变体的泛化能力的数学分析。在本文中，我们根据分类裕度分析了六个AdaBoost变体的泛化能力。比较的六个变体是Real AdaBoost，Gentle AdaBoost，Modest AdaBoost，Parameterized AdaBoost，Margin-pruning Boost和Penalized AdaBoost。最后，我们使用实验来验证我们的分析。

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  • 来源
    《Journal of electrical and computer engineering》 |2015年第2015期|835357.1-835357.17|共17页
  • 作者

    Shuqiong Wu; Hiroshi Nagahashi;

  • 作者单位

    Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan;

    Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan;

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