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A boosting approach to remove class label noise

机译：的升压的方法来删除类标签噪音

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摘要

Ensemble methods have been known to improve the prediction accuracy over the base learning algorithms. AdaBoost is well-recognized for this in its class. However, it is susceptible to overfitting the training instances corrupted by class label noise. This paper proposes a modification of AdaBoost that is more tolerant to class label noise, which further enhances its ability to boost the prediction accuracy. Particularly, we observe that in Adaboost, the weight-hike of noisy examples can be constrained by careful application of a cut-off in their weights. We study the characteristics of our technique empirically using some artificially generated data set. We also corroborate this on a number of data sets from UCI repository. In both experimental settings, the results obtained affirm the efficiency of our approach. Finally, some of the significant characteristics of our technique related to noisy environments have been investigated.
机译：已知组合方法可以提高基础学习算法的预测精度。 AdaBoost在同类产品中广受认可。但是，它容易过度拟合受类标签噪声破坏的训练实例。本文提出了对AdaBoost的修改，该修改对类标签噪声具有更高的容忍度，从而进一步增强了其提高预测精度的能力。尤其是，我们观察到在Adaboost中，可以通过仔细应用权重的下限来限制嘈杂示例的重量增加。我们使用一些人工生成的数据集经验地研究了我们技术的特征。我们还在UCI存储库中的许多数据集中证实了这一点。在两个实验环境中，获得的结果都证实了我们方法的有效性。最后，我们研究了与噪音环境有关的我们技术的一些重要特征。

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