A novel three-way distance-based fuzzy large margin distribution machine for imbalance classification

Abstract

Class imbalance is a prevalent issue in practical applications, which poses significant challenges for classifiers. The large margin distribution machine (LDM) introduces the margin distribution of samples to replace the traditional minimum margin, resulting in extensively enhanced classification performance. However, the hyperplane of LDM tends to be skewed toward the minority class, due to the optimization property for margin means. Moreover, the absence of non-deterministic options and measurement of the confidence level of samples further restricts the capability to manage uncertainty in imbalanced classification tasks. To solve these problems, we propose a novel three-way distance-based fuzzy large margin distribution machine (3W-DBFLDM). Specifically, we introduce a distance-based factor to mitigate the impact of sample size imbalance on classification results by increasing the distance weights of the minority class. Additionally, three-way decision model is introduced to deal with uncertainty, and the model’s robustness is further enhanced by utilizing the fuzzy membership degree that reflects the importance level of each input point. Comparative experiments conducted on UCI datasets demonstrate that the 3W-DBFLDM model surpasses other models in classification accuracy, stability, and robustness. Furthermore, the cost comparison experiment validate that the 3W-DBFLDM model reduces the overall decision cost.