发表机构
Indian Institute of Technology Indore（印度印多尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出KRPRVFL模型，结合RVFL效率与KRP准则鲁棒性，引入协作学习机制，经UCI和KEEL数据集实验，其在分类任务中性能优于基线模型。
AI 中文摘要
随机向量函数链接（RVFL）网络是轻量且快速的神经模型，通过随机隐藏层权重与直接输入输出连接实现高效训练与强泛化性。然而，传统RVFL模型对噪声标签、异常值和不平衡数据敏感，限制了其在实际应用中的性能。为应对这些挑战，本文提出基于核风险敏感均值p幂的RVFL（KRPRVFL）模型，将RVFL的计算效率与核风险敏感均值p幂（KRP）准则的鲁棒性相结合。通过用基于KRP的损失替代标准最小二乘目标，KRPRVFL在训练过程中自适应降低损坏或不可靠样本的影响，提升稳定性与泛化性。此外，引入协作学习机制以实现模型组件间的自适应交互，进一步增强复杂噪声环境下的鲁棒性。该框架还利用核诱导特征映射捕捉非线性关系，无需显式隐藏层选择，兼顾效率与可扩展性。在UCI和KEEL基准数据集上的大量实验表明，KRPRVFL在准确率、鲁棒性和统计显著性方面均优于基线模型，凸显其作为快速、可扩展且可靠的挑战性分类任务解决方案的有效性。
英文摘要
Random vector functional link (RVFL) networks are lightweight and fast neural models that offer efficient training and strong generalization through randomized hidden-layer weights and direct input-output connections. However, conventional RVFL models are sensitive to noisy labels, outliers, and imbalanced data, which limits their performance in real-world applications. To address these challenges, we propose the kernel risk-sensitive mean p-power based RVFL (KRPRVFL) model, which integrates the computational efficiency of RVFL with the robustness of the kernel risk-sensitive mean p-power (KRP) criterion. By replacing the standard least-squares objective with a KRP-based loss, KRPRVFL adaptively reduces the influence of corrupted or unreliable samples during training, resulting in improved stability and generalization. Additionally, a collaborative learning mechanism is introduced to enable adaptive interaction among model components, further enhancing robustness in complex and noisy environments. The proposed framework also leverages kernel-induced feature mapping to capture nonlinear relationships without requiring explicit hidden-layer selection, maintaining both efficiency and scalability. Extensive experiments on UCI and KEEL benchmark datasets demonstrate that KRPRVFL consistently outperforms baseline models in terms of accuracy, robustness, and statistical significance, highlighting its effectiveness as a fast, scalable, and reliable solution for challenging classification tasks.
Journal refIEEE World Congress on Computational Intelligence (WCCI), 2026
