A Three-Way Incremental Granular-Ball Classifier Using Shadowed Set

doi:10.1109/TETCI.2026.3657948

A Three-Way Incremental Granular-Ball Classifier Using Shadowed Set

2026-02-06

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DOI：10.1109/TETCI.2026.3657948

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Incremental learning has emerged as a vital paradigm in machine learning, enabling models to adaptively learn from streaming or continuously arriving data without retraining from scratch. However, incremental learning faces two fundamental challenges: the uncertainty in datasets and the efficiency to handle massive data volumes. Granular-ball computing (GBC), as a recent development in granular computing, excels in rapidly generating robust and scalable information granules, called granular-balls (GBs), that naturally support coarse-to-fine data approximation. By integrating GBC with incremental learning, we propose an adaptive model incremental learning with granular-ball shadowed sets (ILGBSS). ILGBSS constructs and updates GBs incrementally, minimizing the need for frequent structural adjustments such as granular-ball (GB) splitting. To further improve the model’s robustness in uncertain environments, we extend ILGBSS with three-way decision theory, resulting in ILGBSS with three-way classification (ILGBSS-3WC). We conduct experiments on sixteen benchmark datasets, comparing our methods with three state-of-the-art GB-based algorithms and three widely used incremental learning models. The results consistently demonstrate that ILGBSS-3WC classifiers achieve superior classification accuracy and computational efficiency, validating the effectiveness of our approach in both static and evolving data environments.

Keywords:

Granular-ball computing

incremental learning

shadowed set

three-way classification

A Three-Way Incremental Granular-Ball Classifier Using Shadowed Set

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