<ul><li>A fast and stable granular ball generation method (GBG++) for classification is proposed in this paper.</li><li>GBG++ method improves the stability and efficiency of existing granular ball generation methods by using the attention mechanism.</li><li>The proposed GBG++ method calculates the distances from the data-driven center to undivided samples, resulting in improved effectiveness, robustness, and efficiency.</li><li>Experimental results show that the GBG++ method outperforms existing granular ball-based classifiers and classical machine learning classifiers on 24 public benchmark datasets.</li></ul>

GBG++: A Fast and Stable Granular Ball Generation Method for Classification

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