Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection

TitleAddressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection
Publication TypeConference Paper
Year of Publication2015
AuthorsFernández, Alberto, del Jesus M. J., and Herrera Francisco
EditorJackowski, Konrad, Burduk Robert, Walkowiak Krzysztof, Wozniak Michal, and Yin Hujun
Conference NameIntelligent Data Engineering and Automated Learning – IDEAL 2015
Pagination36–44
PublisherSpringer International Publishing
Conference LocationCham
ISBN Number978-3-319-24834-9
Abstract

In classification tasks with imbalanced datasets the distribution of examples between the classes is uneven. However, it is not the imbalance itself which hinders the performance, but there are other related intrinsic data characteristics which have a significance in the final accuracy. Among all, the overlapping between the classes is possibly the most significant one for a correct discrimination between the classes.