SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data

arXiv:2403.05918v1 Announce Type: new
Abstract: 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 …

abstract arxiv balance classification cs.ai cs.lg data data mining ddpm diffusion distribution generate learn machine machine learning mining modelling network residual small solve training type