Robust gravitation based adaptive [formula omitted]-NN graph under class-imbalanced scenarios

Abstract

As a typical single parametric graph model, the k-Nearest Neighbor Graph (k-NNG) is characterized by its high efficiency in detecting topology information and remarkable capability for combining global and local features. However, graph-based supervised learning methods, including k-NNG, do not explicitly consider imbalanced class distribution; as a result, they often tend to perform vulnerable robustness in most imbalanced scenarios. This paper presents an adaptive k-NNG-based imbalanced classification method that can automatically determine the k value during graph construction. Our work presents two novel methodologies: (i) two types of adaptive graph construction methods to address the inherent complex characteristic of imbalanced class distribution; (ii) two graph-based gravitational classification rules to overcome the adverse bias towards the majority class in traditional KNN-based methods. The latter can effectively combine local nearest neighbors and subgraph information when calculating the gravitational force between pairs of vertices. Extensive experiments demonstrate the superiority of our method over other classification algorithms in imbalanced scenarios.