统一鲁棒提升
Unified Robust Boosting
AI总结：
本文提出迭代重加权提升（IRBoost）算法，将鲁棒非凸损失优化与观测权重估计相结合，以识别离群值并提升模型鲁棒性，已在R包irboost中实现并验证。
摘要：
提升（Boosting）是监督机器学习中一种流行的算法，在回归和分类问题中有广泛应用。它通过组合弱学习器（如回归树）来获得准确的预测。然而，在存在离群值的情况下，传统提升方法可能会产生较差的结果，因为该算法优化的是凸损失函数。近期文献提出了优化鲁棒非凸损失函数的提升算法。尽管如此，目前仍缺乏用于指示观测值离群状态的加权估计方法。本文介绍了迭代重加权提升（IRBoost）算法，该算法结合了鲁棒损失优化和加权估计。它可以方便地利用现有软件进行构建。其输出包括权重，可作为判断观测值离群状态的有价值的诊断信息。对于对提升算法感兴趣的实践者来说，这一新方法可以理解为一种调整鲁棒观测权重的方式。IRBoost已在R包irboost中实现，并利用公开可用的数据在广义线性模型、分类和生存数据分析中进行了演示。
英文摘要：
Boosting is a popular algorithm in supervised machine learning with wide applications in regression and classification problems. It combines weak learners, such as regression trees, to obtain accurate predictions. However, in the presence of outliers, traditional boosting may yield inferior results since the algorithm optimizes a convex loss function. Recent literature has proposed boosting algorithms that optimize robust nonconvex loss functions. Nevertheless, there is a lack of weighted estimation to indicate the outlier status of observations. This article introduces the iteratively reweighted boosting (IRBoost) algorithm, which combines robust loss optimization and weighted estimation. It can be conveniently constructed with existing software. The output includes weights as valuable diagnostics for the outlier status of observations. For practitioners interested in the boosting algorithm, the new method can be interpreted as a way to tune robust observation weights. IRBoost is implemented in the R package irboost and is demonstrated using publicly available data in generalized linear models, classification, and survival data analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
