In the field of data mining and machine learning, commonly used
classification models cannot effectively learn in unbalanced data. In order to
balance the data distribution before model training,oversamplingmethods are
often used to generate data for a small number of classes to solve the problem
of classifying unbalanced data. Most of the classical oversampling methods are
based on theSMOTE technique, which only focuses on the local information of the
data, and therefore the generated data may have the problem of not being
realistic enough. In the current oversampling methods based on generative
networks, the methods based on GANs can capture the true distribution of data,
but there is the problem of pattern collapse and training instability in
training; in the oversampling methods based on denoising diffusion probability
models, the neural network of the inverse diffusion process using the U-Net is
not applicable to tabular data, and although the MLP can be used to replace the
U-Net, the problem exists due to the simplicity of the structure and the poor
effect of removing noise. problem of poor noise removal. In order to overcome
the above problems, we propose a novel oversampling method SEMRes-DDPM.In the
SEMRes?DDPM backward diffusion process, a new neural network structure
SEMST-ResNet is used, which is suitable for tabular data and has good noise
removal effect, and it can generate tabular data with higher quality.
Experiments show that the SEMResNet network removes noise better than MLP;
SEMRes?DDPM generates data distributions that are closer to the real data
distributions than TabDDPM with CWGAN-GP; on 20 real unbalanced tabular
datasets with 9 classification models, SEMRes-DDPM improves the quality of the
generated tabular data in terms of three evaluation metrics (F1, G-mean, AUC)
with better classification performance than other SOTA oversampling methods.

基于 SEMRes-DDPM 的新型过采样方法有效提高了标签不平衡的表格数据生成质量，并在分类性能上胜过其他 SOTA 过采样方法。