Boosting is a popular machine learning algorithm in regression and classification problems. Boosting can combine a sequence of regression trees to obtain accurate prediction. In the presence of outliers, traditional boosting, based on optimizing convex loss functions, may show inferior results. In this article, a unified robust boosting is proposed for more resistant estimation. The method utilizes a recently developed concave-convex family for robust estimation, composite optimization by conjugation operator, and functional decent boosting. As a result, an iteratively reweighted boosting algorithm can be conveniently constructed with existing software. Applications in robust regression, classification and Poisson regression are demonstrated in the R package ccboost.
翻译：推力是一种在回归和分类问题上流行的机器学习算法。 推力可以结合一系列回归树以获得准确的预测。 在有外部线的情况下, 以优化 convex 损失功能为基础的传统推力可能会显示低效。 在本条中, 提议采用统一的强力推力来进行更具抗力的估算。 该方法使用最近开发的 concave- convex 组合进行稳健的估计、 由 conjugation 操作员进行复合优化, 以及功能上体面的推力。 因此, 迭代重加权推力算法可以方便地用现有软件构建。 R 包的 ccboost 中展示了强力回归、 分类 和 Poisson 回归的应用 。
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