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The paper proposes intuitionistic fuzzy deep RVFL (IF-dRVFL) and ensemble deep RVFL (IF-edRVFL) frameworks that use sample neighborhood information to improve robustness against noise and outliers in classification tasks, outperforming existing SOTA fuzzy and non-fuzzy approaches on benchmark datasets.
本文提出了基于直觉模糊图嵌入的多视图学习随机向量函数链接模型（IFGRVFL-MV），该模型融合了直觉模糊集、图嵌入和多视图学习，以提高分类精度和对异常值的鲁棒性。在基准数据集上的实验表明，IFGRVFL-MV优于现有模型。