发表机构
Indian Institute of Technology Indore（印度英迪尔印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数据不确定性下分类任务的鲁棒性问题，本文提出带Wave损失的Wave-BLS框架，在30个UCI数据集上的实验显示其性能优于经典BLS及多种鲁棒变体，鲁棒性显著提升。
AI 中文摘要
宽学习系统（Broad Learning System, BLS）通过随机特征映射和闭式解实现快速学习，是深度架构的高效替代方案。但其依赖平方误差损失，对噪声、异常值和损坏标签高度敏感，限制了在真实场景中的可靠性。为解决该问题，本文提出Wave-BLS，一种集成了Wave损失函数的鲁棒宽学习框架；Wave损失具有非对称、有界、平滑的特性，可对大误差进行可控惩罚。该方法用基于Wave损失的优化问题替代标准最小二乘目标，采用基于Nesterov加速梯度（NAG）的方案高效求解，无需矩阵求逆，提升了可扩展性。在30个UCI基准数据集上的大量实验表明，Wave-BLS的性能始终优于经典BLS及多种鲁棒变体；通过Friedman和Nemenyi事后检验的统计验证，确认了观测到的改进具有显著性。此外，在受控噪声和异常值注入下的鲁棒性评估显示，即使在具有挑战性的污染场景中，Wave-BLS的性能下降也远慢于BLS。这些结果证明，Wave-BLS是现有宽学习模型的稳定且鲁棒的替代方案，适用于数据不确定性下的学习任务。
英文摘要
Broad Learning System (BLS) offers an efficient alternative to deep architectures by enabling fast learning through randomized feature mapping and closed-form solutions. However, its reliance on squared error loss makes it highly sensitive to noise, outliers, and corrupted labels, limiting its reliability in real-world scenarios. To address this limitation, we propose Wave-BLS, a robust broad learning framework that integrates the wave loss function, which is asymmetric, bounded, and smooth, enabling controlled penalization of large errors. The proposed formulation replaces the standard least-squares objective with a wave-loss-based optimization problem, solved efficiently using a Nesterov accelerated gradient (NAG)-based scheme without requiring matrix inversion, thereby improving scalability. Extensive experiments on 30 UCI benchmark datasets demonstrate that Wave-BLS consistently outperforms classical BLS and several robust variants. Statistical validation using Friedman and Nemenyi post-hoc tests confirms the significance of the observed improvements. Furthermore, robustness evaluations under controlled noise and outlier injection reveal that Wave-BLS exhibits substantially slower performance degradation compared to BLS, even in challenging contamination settings. These results establish Wave-BLS as a stable and robust alternative to existing broad learning models for learning under data uncertainty.
CommentsAccepted at WCCI 2026
