Synthetic sampling from small datasets: A modified mega-trend diffusion approach using k-nearest neighbors

Highlights：

• Tackle small dataset challenges for both supervised and unsupervised learning tasks using synthetic data generation.

• A nearest neighbor-based megatrend diffusion is proposed in this research.

• The proposed method generates synthetic data for both supervised and unsupervised learning tasks.

• Focuses on retaining the attribute relations similar to the original dataset, reducing the information gap.

• The training data is used only to identify the domain ranges which in turn improves the privacy of any sensitive data.