发表机构
Indian Institute of Technology Indore（印度印多尔印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对类别不平衡与噪声数据问题，提出RoBell-RVFL模型，采用双策略样本级加权机制，在UCI、KEEL数据集及40%标签噪声下表现优于现有RVFL变体。
AI 中文摘要
现实世界数据集中多数类的主导地位对随机神经网络构成了根本挑战，常使决策边界产生偏差并忽略关键的少数类样本。现有补救措施如合成少数类过采样（SMOTE）和类别加权损失函数，主要解决类别比例问题却忽略了类别内分布，易受标签噪声和异常值影响。本文提出RoBell-RVFL，一种鲁棒且轻量的质量感知广义钟型随机向量函数链接网络，重新定义随机模型处理类别不平衡和噪声数据的方式。RoBell-RVFL采用双策略样本级加权机制，通过单位权重严格保留少数类信息，同时在核诱导特征空间中，通过概率加权的广义钟型（gbell）隶属函数自适应调节多数类样本的影响。该设计有效抑制多数类中的噪声、边界及异常值样本，使网络从信息丰富的样本而非仅从数量多的样本中学习。通过在学习过程中明确融入局部类别概率和类别分布信息，RoBell-RVFL实现了对样本贡献的自适应控制，同时不损失RVFL网络的闭式学习效率。在UCI和KEEL基准数据集上的大量评估，以及在高达40%标签噪声下的鲁棒性测试表明，RoBell-RVFL始终显著优于近期最先进的RVFL变体。结果表明，自适应的质量感知样本加权对于鲁棒RVFL学习至关重要，传统的全局加权方案在噪声和不平衡环境中无效。
英文摘要
The dominance of majority classes in real-world datasets poses a fundamental challenge to randomized neural networks, often biasing decision boundaries and overlooking critical minority samples. Existing remedies, such as synthetic minority over-sampling (SMOTE) and class-weighted loss functions, primarily address class proportions while neglecting intra-class distribution, making them vulnerable to label noise and outliers. In this paper, we propose \textbf{RoBell-RVFL}, a robust and lightweight \emph{quality-aware} generalized bell random vector functional link network that redefines how randomized models handle class imbalance and noisy data. RoBell-RVFL employs a dual-strategy, sample-level weighting mechanism that strictly preserves minority class information using unit weights, while adaptively regulating the influence of majority class samples through a probability-weighted generalized bell (gbell) membership function in a kernel-induced feature space. This design effectively suppresses noisy, boundary, and outlier samples within the majority class, enabling the network to learn from informative samples rather than merely abundant ones. By explicitly incorporating local class probability and class distribution information into the learning process, RoBell-RVFL achieves adaptive control over sample contributions without sacrificing the closed-form learning efficiency of RVFL networks. Extensive evaluations on UCI and KEEL benchmark datasets, along with robustness tests under up to 40\% label noise, demonstrate that RoBell-RVFL consistently and significantly outperforms recent state-of-the-art RVFL variants. The results indicate that adaptive, quality-aware sample weighting is essential for robust RVFL learning, rendering conventional global weighting schemes ineffective in noisy and imbalanced environments.
Journal refIEEE World Congress on Computational Intelligence (WCCI), 2026
