Data reduction based on fuzzy rough sets

As datasets are getting more and more high-dimensional, there is an emerging need for data reduction techniques. This project focuses both on feature selection that selects relevant features and instance selection that removes irrelevant or noisy instances. More specifically, we work on fuzzy rough data reduction, data reduction for imbalanced datasets and robust data reduction.