论文标题
部分可观测时空混沌系统的无模型预测
Binary and Multinomial Classification through Evolutionary Symbolic Regression
论文作者
论文摘要
我们提供了三种基于二进制和多项式数据集的基于进化符号回归的分类算法：GpleArnClf，CartesianClf和Clasyco。测试了超过162个数据集，并与三种最先进的机器学习算法进行了比较 - XGBoost，LightGBM和一个深神经网络 - 我们发现我们的算法具有竞争力。此外，我们通过使用最先进的超参数优化器来演示如何自动找到数据集的最佳方法。
We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf, CartesianClf, and ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art machine learning algorithms -- XGBoost, LightGBM, and a deep neural network -- we find our algorithms to be competitive. Further, we demonstrate how to find the best method for one's dataset automatically, through the use of a state-of-the-art hyperparameter optimizer.
