Geometric relative margin machine for heterogeneous distribution and imbalanced classification

Xiao-Jing Lv, Ling-Wei Huang, Yuan-Hai Shao

Abstract: This paper introduces the geometric relative margin machine (GRMM) to address class imbalance and heterogeneous data distributions in classification tasks. GRMM innovatively incorporates boundary information as distribution information and implements differentiated misclassification cost strategies for each class, allowing adaptive adjustments to the decision hyperplane and enhancing robustness against outliers. The model employs a block iterative algorithm for optimization. Experimental results on artificial, benchmark, and credit risk assessment datasets demonstrate that GRMM significantly outperforms other benchmark methods in terms of classification accuracy and robustness. This research provides a comprehensive and effective solution to imbalanced classification problems, with potential applications in real-world scenarios, such as credit assessment.