Noise-robust oversampling for imbalanced data classification
作者：
Highlights：
• Propose three noise-robust mechanisms to address the noise generation problem in classic oversampling algorithms: adopting an advanced clustering algorithm, designing adaptive embedding to generate samples, and implementing a safe boundary to enlarge class boundaries.
• Propose the heterogeneous distance metric to better cluster mixed-type data along with dedicated approaches to avoid generating groundless samples with categorical variables.
• Adapted decomposition strategy extends solution for binary imbalanced data to the multi-class setting. Moreover, better placement of new samples are provided.
• Experiments on the standard datasets validate the effectiveness of the proposed data.
摘要
•Propose three noise-robust mechanisms to address the noise generation problem in classic oversampling algorithms: adopting an advanced clustering algorithm, designing adaptive embedding to generate samples, and implementing a safe boundary to enlarge class boundaries.•Propose the heterogeneous distance metric to better cluster mixed-type data along with dedicated approaches to avoid generating groundless samples with categorical variables.•Adapted decomposition strategy extends solution for binary imbalanced data to the multi-class setting. Moreover, better placement of new samples are provided.•Experiments on the standard datasets validate the effectiveness of the proposed data.
论文关键词：Imbalanced learning,Classification,Clustering
论文评审过程：Received 26 May 2021, Revised 13 August 2022, Accepted 27 August 2022, Available online 6 September 2022, Version of Record 16 September 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.109008
