GBkNN-JGE: Enhancing GBkNN via Principle of Justifiable Granularity and Ensemble Learning

Abstract

Achieving efficiency, robustness and interpretability in classification continues to pose significant challenges in data analysis. To address these issues, the granular-ball-based k-nearest neighbors (GBkNN) classifier has recently been proposed and demonstrated promising results. However, the performance of GBkNN heavily relies on the quality of GBs, and existing GB generation methods often use only purity as the evaluation criterion and apply a fixed threshold-based stopping rule. These limitations restrict their effectiveness in practical applications. To overcome this, we extend the advanced unsupervised GB generation method based on the Principle of Justifiable Granularity (POJG) to the supervised classification setting, aiming to enhance the overall performance of GBkNN. Furthermore, to mitigate the instability caused by the inherent randomness of