Novel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment

作者：

Highlights：

• We propose a new clustering validity index for credit risk assessment problems.

• We provide to realize the clustering of credit datasets by a multi-objective evolutionary optimization algorithm.

• We introduce a GPU-based parallel computation framework for our proposed. algorithm.

• We conduct a comprehensive experiment on our proposed approach and get promising experimental results.

摘要

•We propose a new clustering validity index for credit risk assessment problems.•We provide to realize the clustering of credit datasets by a multi-objective evolutionary optimization algorithm.•We introduce a GPU-based parallel computation framework for our proposed. algorithm.•We conduct a comprehensive experiment on our proposed approach and get promising experimental results.

论文关键词：Credit risk assessment,High-dimensional data,Soft subspace clustering,Evolutionary multi-objective algorithm,ASEA

论文评审过程：Received 25 June 2018, Revised 1 July 2019, Accepted 18 July 2019, Available online 20 July 2019, Version of Record 24 July 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112827