论文标题
主动学习改善了stackGP中符号回归任务的性能
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP
论文作者
论文摘要
在本文中，我们介绍了一种使用StackGP的主动学习方法，用于符号回归。该方法以少量的数据点开始，用于建模。为了改善模型，系统会逐步添加一个数据点，以使新点最大化模型集合测量的预测不确定性。符号回归是通过较大的数据集重新运行的。该周期一直持续到系统满足终止标准为止。我们使用Feynman AI基准方程组来检查我们方法使用较少数据点找到适当模型的能力。发现该方法成功地重新发现了100个Feynman方程中的72，使用尽可能少的数据点，并且不使用域专业知识或数据翻译。
In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds a data point such that the new point maximizes prediction uncertainty as measured by the model ensemble. Symbolic regression is re-run with the larger data set. This cycle continues until the system satisfies a termination criterion. We use the Feynman AI benchmark set of equations to examine the ability of our method to find appropriate models using fewer data points. The approach was found to successfully rediscover 72 of the 100 Feynman equations using as few data points as possible, and without use of domain expertise or data translation.
