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

• Published in: IEEE transactions on emerging topics in computational intelligence Vol. 8; no. 2; pp. 2022 - 2036
• Main Authors: Xie, Qin, Zhang, Qinghua, Xia, Shuyin, Zhao, Fan, Wu, Chengying, Wang, Guoyin, Ding, Weiping
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomaly detection; Classification; Classifiers; Computation; Computational intelligence; Computer architecture; Data analysis; granular ball computing; Granular computing; label noise; Machine learning; Machine learning algorithms; Mathematical analysis; multi-granularity learning; Outliers (statistics); Support vector machines; Training
• ISSN: 2471-285X; 2471-285X
• DOI: 10.1109/TETCI.2024.3359091

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 <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-means or <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-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 <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-nearest neighbors algorithm (GB<inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>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.