Fast knot optimization for multivariate adaptive regression splines using hill climbing methods

作者：

Highlights：

• Two novel methods, PHCM and HCM, for MARS knot positioning are proposed.

• Traditional and two novel methods are similarly accurate and robust with noise.

• The proposed methods are 10% to 70% faster than traditional method.

• PHCM is 70% faster than HCM for higher dimensional datasets.

• The Python code will be made open-source on GitHub to help other researchers.

摘要

•Two novel methods, PHCM and HCM, for MARS knot positioning are proposed.•Traditional and two novel methods are similarly accurate and robust with noise.•The proposed methods are 10% to 70% faster than traditional method.•PHCM is 70% faster than HCM for higher dimensional datasets.•The Python code will be made open-source on GitHub to help other researchers.

论文关键词：MARS,Regression,Knot optimization,Knot positioning,Hill climbing

论文评审过程：Received 14 October 2019, Revised 1 December 2020, Accepted 31 December 2020, Available online 7 January 2021, Version of Record 6 February 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114565