当代象征回归方法及其相对性能
William La Cava1, Bogdan Burlacu2, Marco Virgolin3
1Boston Children's Hospital, Harvard Medical School.
概括
本研究介绍了一个可复制的符号回归的基准测试平台,对252个问题评估了14种方法. 现实数据的最佳方法是将遗传算法与参数估计或语义搜索相结合.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 符号回归 (SR) 缺乏标准化的基准测试,阻碍了进展.
- 现有的SR方法需要对各种问题进行强有力的评估.
- 透明和可重复的基准对于推进SR至关重要.
研究的目的:
- 引入一个开源的,可复制的对比测试平台,用于符号回归.
- 提供SR方法的标准化评估框架.
- 促进SR技术的合作开发和改进.
主要方法:
- 评估了14种符号回归 (SR) 方法和7种机器学习 (ML) 方法.
- 利用了252个不同的回归问题,包括真实世界和合成数据集的基准套件.
- 对模型准确性,复杂性和在噪音下恢复精确方程的能力进行评估.
主要成果:
- 对于现实数据集,将遗传算法与参数估计或语义搜索相结合的SR方法表现最好.
- 在与噪声有关的合成问题上,几种方法在恢复精确方程方面表现相似.
- 开发的平台允许对SR算法进行可重复的评估和比较.
结论:
- 一个标准化,开源的基准测试平台对于象征回归研究至关重要.
- 混合方法 (遗传算法 + 参数估计 / 语义搜索) 对现实世界的SR有效.
- 鼓励进一步合作,开发一个象征性回归的活生生的基准.
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