A preliminary study on overlapping and data fracture in imbalanced domains by means of Genetic Programming-based feature extraction

The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory to help visualize the data in a bidimensional graph, to better understand how the presence of overlapping and data fractures affect classification performance.