发表机构
Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对广义学习系统在处理含噪声和多源数据时的不足，提出多视图图嵌入直觉模糊广义学习系统，融合多视图学习、图嵌入和直觉模糊理论，经多方法评估，该系统在多个数据集上表现良好，AUC分数高且性能稳健。
AI 中文摘要
广义学习系统（BLS）基于逐层前馈结构，广泛用于数据分类。但它对所有数据点同等重视，在含噪声和离群值的真实世界数据集上效果不佳，且未考虑数据几何结构，处理多源数据有局限。为应对这些挑战，我们提出多视图图嵌入直觉模糊广义学习系统（MVGIFBLS），将多视图学习、图嵌入和直觉模糊理论集成到BLS框架中。该设计使模型能融合多源信息，学习更具判别力的表示。图嵌入捕捉样本几何关系，基于局部Fisher判别分析通过内在和惩罚子空间改善类分离。直觉模糊理论增强对噪声的鲁棒性，基于核的邻域分析捕捉局部数据结构。我们使用比较评估、高斯特征噪声分析、消融研究和统计分析在多个UCI、KEEL和AwA基准数据集上评估该框架。结果表明各组件对整体框架有积极贡献，所提出的MVGIFBLS始终获得更高的曲线下面积（AUC）分数，并在高斯特征噪声下保持稳健性能。
英文摘要
The Broad Learning System (BLS) has been widely used for data classification and is based on a layer-by-layer feed-forward structure. However, it gives the same importance to all data points, which reduces its effectiveness on real-world datasets with noise and outliers. In addition, it does not consider the geometric structure of the data and has limitations in handling data from multiple sources. To address these challenges, we propose a Multi-View Graph-Embedded Intuitionistic Fuzzy Broad Learning System (MVGIFBLS) that integrates multi-view learning, graph embedding, and intuitionistic fuzzy theory into the BLS framework. This design enables the model to combine information from multiple sources and learn more discriminative representations. Graph embedding captures the geometric relationships among samples and improves class separation through intrinsic and penalty subspaces based on local Fisher discriminant analysis. Intuitionistic fuzzy theory enhances robustness to noise, while kernel-based neighborhood analysis captures local data structures. We evaluate the proposed framework on several UCI, KEEL, and AwA benchmark datasets using comparative evaluation, Gaussian feature noise analysis, ablation studies, and statistical analysis. The results demonstrate that each component contributes positively to the overall framework and that the proposed MVGIFBLS consistently achieves higher Area Under the Curve (AUC) scores and maintains robust performance under Gaussian feature noise.
