Low Sample Size

High-dimensional, low sample size (HDLSS) data, where the number of features far exceeds the number of observations, presents a significant challenge in machine learning. Current research focuses on developing robust classification methods specifically tailored to HDLSS datasets, employing techniques like novel generative adversarial networks (GANs) for data augmentation and kernel methods based on random forest similarities to improve classification accuracy. These advancements are crucial for addressing real-world problems in diverse fields such as medicine and genomics, where data scarcity is often a limiting factor in building effective predictive models.