In order to apply regular learning tools for classification, features have to be extracted out of the data distribution samples. This step is a feature construction step, and the literature has taken three different approaches to extract such features:

• Manual construction of causally relevant features to classify the pairs
• Use embedding of the sample distributions into a fixed size feature vector: the resulting manifold will be mapped to the target classes using the training set, allowing to classify unseen examples using the same embedding.
• Not only use embeddings of distributions, but to also automatically learn and identify classification patterns using the training set.