发表机构
Indian Institute of Technology Indore; Qatar University（印度理工学院印多尔分校; 卡塔尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对噪声、离群点和类别不平衡问题，提出基于粒球计算的模糊随机向量功能链接网络GBFRVFL，通过两种隶属度方案增强鲁棒性，在37个基准数据集上优于基线模型。
AI 中文摘要
在实际机器学习任务中，数据常受到噪声、离群点和类别不平衡的污染，这会降低传统模型的性能。虽然随机向量功能链接（RVFL）网络具有训练速度快和泛化能力强的优点，但它们并未显式处理不确定性或利用局部数据结构。为解决这些局限性，我们提出了一种模糊粒球随机向量功能链接（GBFRVFL）框架，该框架利用粒球计算将原始样本抽象为自适应粒球。在此框架内，我们引入了两种隶属度分配方案：（i）F-GBRVFL，该方案结合模糊隶属度来量化每个粒球的可靠性；（ii）我们提出的SDAP-GBRVFL，该方案引入了一种新颖的统计密度自适应毕达哥拉斯隶属度（SDAPM）方案，该方案根据类别方差、局部稀疏性和粒球紧密度动态调整隶属度和非隶属度值。这些方案增强了对噪声、离群点、类别不平衡以及粒球分布不确定性的鲁棒性，同时保留了RVFL网络的计算效率。在37个基准UCI和KEEL数据集上，在干净和噪声条件下进行的大量实验表明，所提出的模型始终优于基线模型，实现了更高的准确性和稳定性。结果验证了将粒球计算与自适应隶属度方案相结合，用于可靠、可扩展和抗噪声学习的有效性。
英文摘要
In practical machine learning tasks, data are often contaminated with noise, outliers, and class imbalance, which can degrade the performance of conventional models. While random vector functional link (RVFL) networks offer fast training and strong generalization, they do not explicitly handle uncertainty or exploit local data structure. To address these limitations, we propose a fuzzy granular-ball random vector functional link (GBFRVFL) framework that leverages granular-ball computing to abstract raw samples into adaptive granular balls. Within this framework, we introduce two membership assignment schemes: (i) F-GBRVFL, which incorporates fuzzy membership to quantify the reliability of each granular ball, and (ii) SDAP-GBRVFL, which we propose, incorporates a novel statistical density-adaptive pythagorean membership (SDAPM) scheme that dynamically adjusts membership and non-membership values based on class variance, local sparsity, and granular-ball compactness. These schemes enhance robustness to noise, outliers, class imbalance, and uncertainty in granular-ball distributions, while retaining the computational efficiency of RVFL networks. Extensive experiments on 37 benchmark UCI and KEEL datasets under both clean and noisy conditions demonstrate that the proposed models consistently outperform baseline models, achieving superior accuracy and stability. The results validate the effectiveness of integrating granular-ball computing with adaptive membership schemes for reliable, scalable, and noise-tolerant learning.
Journal refIEEE World Congress on Computational Intelligence (WCCI), 2026
