Granular ball computing (GBC), as an efficient, robust, and scalable learning
method, has become a popular research topic of granular computing. GBC includes
two stages: granular ball generation (GBG) and multi-granularity learning based
on the granular ball (GB). However, the stability and efficiency of existing
GBG methods need to be further improved due to their strong dependence on
$k$-means or $k$-division. In addition, GB-based classifiers only unilaterally
consider the GB's geometric characteristics to construct classification rules,
but the GB's quality is ignored. Therefore, in this paper, based on the
attention mechanism, a fast and stable GBG (GBG++) method is proposed first.
Specifically, the proposed GBG++ method only needs to calculate the distances
from the data-driven center to the undivided samples when splitting each GB,
instead of randomly selecting the center and calculating the distances between
it to all samples. Moreover, an outlier detection method is introduced to
identify local outliers. Consequently, the GBG++ method can significantly
improve effectiveness, robustness, and efficiency while being absolutely
stable. Second, considering the influence of the sample size within the GB on
the GB's quality, based on the GBG++ method, a $k$-nearest neighbors algorithm
(GB$k$NN++) which can reduce misclassification at class boundary to some extent
is presented. Finally, the experimental results indicate that the proposed
method outperforms several existing GB-based classifiers and classical machine
learning classifiers on $20$ public benchmark data sets.

本文提出了一种基于关注机制的快速稳定 Granular ball 生成方法，并在此基础上开发了 GB$k$NN++，它可将误分类降至一定程度，实验证明此方法在 20 个公共基准数据集上的性能优于现有 GB 基分类器和经典机器学习分类器。