A random grouping-based self-regulating artificial bee colony algorithm for interactive feature detection

作者：

Highlights：

• Random grouping-based self-regulating artificial bee colony algorithm (RCABC) is proposed for interactive feature detection.

• RCABC uses the DRG strategy to decompose the features of a high-dimensional dataset into some low-dimensional subsets.

• RCABC algorithm uses the S-Optimizer to search for relevant interactive features on each subset.

• RCABC is superior to other test algorithms in interactive feature detection.

摘要

•Random grouping-based self-regulating artificial bee colony algorithm (RCABC) is proposed for interactive feature detection.•RCABC uses the DRG strategy to decompose the features of a high-dimensional dataset into some low-dimensional subsets.•RCABC algorithm uses the S-Optimizer to search for relevant interactive features on each subset.•RCABC is superior to other test algorithms in interactive feature detection.

论文关键词：Evolutionary algorithm,Dynamic random grouping,High-dimensional data,Interactive features

论文评审过程：Received 22 July 2021, Revised 6 January 2022, Accepted 9 February 2022, Available online 17 February 2022, Version of Record 2 March 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108434