Tag
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.
This paper proposes the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model, which integrates intuitionistic fuzzy sets, graph embedding, and multiview learning to improve classification accuracy and robustness to outliers. Experiments on benchmark datasets show that IFGRVFL-MV outperforms existing models.