A novel two-phase clustering-based under-sampling method for imbalanced classification problems

作者：

Highlights：

• A novel method is presented to tackle classification problems with imbalanced data.

• It is in the categories of under-sampling data-level and ensemble-based approaches.

• It partitions majority samples into clusters by using the convex-hull concept.

• Computational results confirm the performance of the proposed two-phase method.

摘要

•A novel method is presented to tackle classification problems with imbalanced data.•It is in the categories of under-sampling data-level and ensemble-based approaches.•It partitions majority samples into clusters by using the convex-hull concept.•Computational results confirm the performance of the proposed two-phase method.

论文关键词：Imbalanced data,Under-sampling,Clustering,Convex-hull,Ensemble learning

论文评审过程：Received 5 June 2022, Revised 26 September 2022, Accepted 9 October 2022, Available online 14 October 2022, Version of Record 20 October 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.119003