Abstract: A method performed by a node for handling drift in data. The node obtains a dataset including a plurality of datapoints corresponding to a plurality of values of one or more dependent variables for a plurality of first features over a time period. The node determines, using machine learning and explainability, in the absence of determining whether or not the plurality of datapoints has a drift, whether or not there has been a change in respective one or more characteristics of a subset of the plurality of first features having a largest contribution to a variability of the datapoints in the plurality of datapoints based on a threshold from a first time period to a second time period. The node then initiates application of a drift policy on the plurality of datapoints based on a result of the determination.

Abstract: A method performed by a first node (111) for handling data augmentation. The first node (111) divides (201) each epoch in an original dataset having an input space, into a set of batches. The first node (111) generates (202) a set of subsets of samples by selecting, within each batch from every set of batches, a respective plurality of subsets. The first node (111) determines (203), using machine learning, a fourth set of clusters of data using the third set. The first node (111) selects (204) a fifth set of clusters from the fourth set based on a relevance criterion. The first node (111) generates (205) samples in each cluster of the fifth set, and refrains from generating samples in clusters of the fourth set excluded from the fifth set. The first node (111) then generates (206) a sixth set of augmented samples in the input space of the original dataset, by using the generated samples and applying a reverse projection approach.