LDAS: Local density-based adaptive sampling for imbalanced data classification
作者：
Highlights：
• New adaptive weighted sampling method for imbalanced data classification.
• Controls the sampling process effectively using local density of minority class.
• Cleans overlaps by considering local neighborhood information of minority class.
• Synthesizes samples by utilizing both border and safe information simultaneously.
• It may alleviate potential over-fitting caused by normal over-sampling methods.
摘要
•New adaptive weighted sampling method for imbalanced data classification.•Controls the sampling process effectively using local density of minority class.•Cleans overlaps by considering local neighborhood information of minority class.•Synthesizes samples by utilizing both border and safe information simultaneously.•It may alleviate potential over-fitting caused by normal over-sampling methods.
论文关键词：Imbalanced classification,Local density,Overlapping data,Re-sampling
论文评审过程：Received 6 November 2020, Revised 6 October 2021, Accepted 7 November 2021, Available online 22 November 2021, Version of Record 2 December 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116213
