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

• URL: http://arxiv.org/abs/2305.18450v2
• Date: Mon, 13 Nov 2023 15:09:49 GMT
• Title: GBG++: A Fast and Stable Granular Ball Generation Method for Classification
• Authors: Qin Xie, Qinghua Zhang, Shuyin Xia, Fan Zhao, Chengying Wu, Guoyin Wang and Weiping Ding
• Abstract summary: Granular ball computing is an efficient, robust, and scalable learning method. The stability and efficiency of existing GBG methods need to be further improved. A fast and stable GBG (GBG++) method is proposed first.
• Score: 18.611701583873504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 and 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, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on $24$ public benchmark datasets.

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