论文标题
Bootstrap偏差校正了应用于超级学习的交叉验证
Bootstrap Bias Corrected Cross Validation applied to Super Learning
论文作者
论文摘要
超级学习者算法可以应用于结合多个基础学习者的结果以提高预测质量。验证超级学习者结果的默认方法是通过嵌套交叉验证。 Tsamardinos等人已经提出，可以通过重新采样来调整学习算法的超参数来代替嵌套的交叉验证。我们将此想法应用于超级学习者的验证，并与其他验证方法（包括嵌套交叉验证）进行比较。对各种大小的人工数据集和七个真实的生物医学数据集进行了测试。被称为Bootstrap偏置校正的重采样方法被证明是嵌套交叉验证的合理精确且非常具有成本效益的替代方法。
Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation. It has been proposed by Tsamardinos et al., that nested cross validation can be replaced by resampling for tuning hyper-parameters of the learning algorithms. We apply this idea to verification of super learner and compare with other verification methods, including nested cross validation. Tests were performed on artificial data sets of diverse size and on seven real, biomedical data sets. The resampling method, called Bootstrap Bias Correction, proved to be a reasonably precise and very cost-efficient alternative for nested cross validation.
