DEBOHID: A differential evolution based oversampling approach for highly imbalanced datasets

作者：

Highlights：

• A novel oversampling method based on a DEBOHID is presented.

• SVM, k-NN, and DT are used as a classifier.

• The independence of the experimental results to the classifier is showed.

• AUC and G-Mean are used as performance metrics for determining the performance.

• The experiments have shown the superiority of DEBOHID for rare events detection.

摘要

•A novel oversampling method based on a DEBOHID is presented.•SVM, k-NN, and DT are used as a classifier.•The independence of the experimental results to the classifier is showed.•AUC and G-Mean are used as performance metrics for determining the performance.•The experiments have shown the superiority of DEBOHID for rare events detection.

论文关键词：Imbalanced data learning,Differential evolution,Oversampling,Class imbalance

论文评审过程：Received 20 July 2020, Revised 26 November 2020, Accepted 7 December 2020, Available online 13 December 2020, Version of Record 24 December 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114482