Noise-robust oversampling for imbalanced data classification

•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.