Incremental updating approximations for double-quantitative decision-theoretic rough sets with the variation of objects

作者：

Highlights：

• We study dynamic maintenance approaches for the approximations of Dq-DTRS.

• We propose incremental updating mechanisms of Dq-DTRS with the variation of objects.

• We design incremental sequential and batch updating algorithms for Dq-DTRS models.

• The validity, stability and efficiency of our methods are verified by comparisons.

摘要

•We study dynamic maintenance approaches for the approximations of Dq-DTRS.•We propose incremental updating mechanisms of Dq-DTRS with the variation of objects.•We design incremental sequential and batch updating algorithms for Dq-DTRS models.•The validity, stability and efficiency of our methods are verified by comparisons.

论文关键词：Double-quantitative decision-theoretic rough sets,Concept approximations,Incremental learning

论文评审过程：Received 5 June 2019, Revised 18 August 2019, Accepted 1 October 2019, Available online 4 October 2019, Version of Record 16 January 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.105082