Using Gabriel graphs in Borderline-SMOTE to deal with severe two-class imbalance problems on neural networks

摘要

In this paper we propose to use Gabriel graphs on standard Borderline-SMOTE, in order to improve its performance on severe two-class imbalance problem in the artificial neural networks context. The standard Borderline-SMOTE shows two drawbacks: 1) it only takes into account the number of neighbors, so information about prototypes distribution is lost. The global classifiers as neural networks need more information to define the borderline decision. 2) The standard Borderline-SMOTE requires a free parameter to find the borderline samples. The advantage of using Gabriel graphs is that it avoids setting free parameters. Empirical results obtained from experiments on real data sets show that the use of Gabriel graphs in Borderline-SMOTE improve the standard Borderline-SMOTE performance on neural networks.