论文标题
凝结的梯度提升
Condensed Gradient Boosting
论文作者
论文摘要
本文介绍了用于多类分类和多出输出回归任务的梯度提升的计算有效变体。标准梯度提升使用1-VS-ALL策略，用于具有两个以上类的分类任务。该策略在每个班级中翻译成一棵树，必须训练迭代。在这项工作中，我们建议将多输出回归器用作基本模型来处理多级问题作为单个任务。此外，提出的修改使模型可以学习多输出回归问题。根据概括和计算效率进行了与其他基于多叶的梯度增强方法进行广泛的比较。提出的方法显示了概括能力和训练和预测速度之间的最佳权衡。
This paper presents a computationally efficient variant of gradient boosting for multi-class classification and multi-output regression tasks. Standard gradient boosting uses a 1-vs-all strategy for classifications tasks with more than two classes. This strategy translates in that one tree per class and iteration has to be trained. In this work, we propose the use of multi-output regressors as base models to handle the multi-class problem as a single task. In addition, the proposed modification allows the model to learn multi-output regression problems. An extensive comparison with other multi-ouptut based gradient boosting methods is carried out in terms of generalization and computational efficiency. The proposed method showed the best trade-off between generalization ability and training and predictions speeds.
