Using information on class interrelations to improve classification of multiclass imbalanced data: A new resampling algorithm - Janicka, Małgorzata; Lango, Mateusz; Stefanowski, Jerzy (Zielona Góra: Uniwersytet Zielonogórski)
The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples` difficulty based on an analysis of the class distribution in the examples` neighborhood, additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass imbalanced datasets.
Keywords: imbalanced data; multi-class learning; re-sampling; data difficulty factors; similarity degrees