论文标题
符号回归的提升
Symbolic-Regression Boosting
论文作者
论文摘要
通过替换嵌入式弱学习者而倾向于强（ER），我们提出syrbo：符号回归的提升来修改标准梯度提升。超过98个回归数据集的实验表明，通过将少量的增强阶段（在2--5之间）添加到符号回归器中，通常可以实现统计学上显着的改进。我们注意到，在任何符号回归器之上编码锡尔布都很简单，而增加的成本只是更多的进化回合。锡尔博本质上是一个简单的附加组件，可以很容易地添加到现存的符号回归器中，通常会带有有益的结果。
Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.
