Undersampling based on generalized learning vector quantization and natural nearest neighbors for imbalanced data

Abstract

Imbalanced datasets can adversely affect classifier performance. Conventional undersampling approaches may lead to the loss of essential information, while oversampling techniques could introduce noise. To address this challenge, we propose an undersampling algorithm called GLNDU (Generalized Learning Vector Quantization and Natural Nearest Neighbors-based Undersampling). GLNDU utilizes Generalized Learning Vector Quantization (GLVQ) for computing the centroids of positive and negative instances. It also utilizes the concept of Natural Nearest Neighbors to identify majority-class instances in the overlapping region of the centroids of minority-class instances. Afterwards, these majority-class instances are removed, resulting in a new balanced training dataset that is used to train a foundational classifier. We conduct extensive experiments on 29 publicly available datasets, evaluating the performance using AUC and G_mean values. GLNDU demonstrates significant advantages over established methods such as SVM, CART, and KNN across different types of classifiers. Additionally, the results of the Friedman ranking and Nemenyi post-hoc test provide additional support for the findings obtained from the experiments.