Pairwise Difference Learning for Classification
Pairwise Difference Learning (PDL) is a meta-learning technique for classification that transforms any problem into a binary one by learning differences between instances. It trains a binary classifier to determine if two instances belong to the same class. This approach significantly improves prediction accuracy, especially on small datasets, outperforming state-of-the-art methods.