Enhancing Classification of Imbalanced Data using Diffusion Process

Abstract

Conventional deep learning algorithms have difficulties when imbalanced data is encountered where one class disproportionately outnumbers another. This work investigates how a novel deep learning approach called diffusion processes could enhance classification performance on unbalanced datasets. We study how well diffusion models can produce artificial information for underrepresented group, equalizing the spread of categories and reducing innate biases in favour of the majority class. The study also looks at how various diffusion model topologies and training approaches affect classification accuracy, with a focus on minority classes. Furthermore, the study intends to evaluate diffusion-based approaches against other cutting-edge strategies for managing unbalanced data. The results of this study should help build more reliable and accurate Deep Learning models for practical uses, as well as offer insightful information on how well diffusion processes work for the generation of unbalanced data.