Robustness of learning techniques in handling class noise in imbalanced datasets
Dionysios Anyfantis, M. G. Karagiannopoulos, Sotiris B. Kotsiantis, Panagiotis Pintelas · 2007
Many real world datasets exhibit skewed class distributions in which almost all instances are allotted to a class and far fewer instances to a smaller, but more interesting class. A classifier induced from an imbalanced dataset has a low error rate for the majority class and an undesirable error rate for the minority class. Many research efforts have been made to deal with class noise but none of them was designed for imbalanced datasets. This paper provides a study on the various methodologies that have tried to handle the imbalanced datasets and examines their robustness in class noise.