Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
For practitioners dealing with noisy or multiview data, this model offers improved robustness and accuracy, though it is an incremental extension of existing techniques.
The paper proposes IFGRVFL-MV, an RVFL variant that integrates intuitionistic fuzzy sets, graph embedding, and multiview learning to handle uncertainty and multiple feature views. Experiments on UCI and KEEL datasets show it outperforms existing models in classification accuracy.
Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving geometric relationships and utilizing multiple feature views effectively. To address these limitations we propose the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model. The proposed approach comprises three key components: intuitionistic fuzzy sets for uncertainty handling, graph embedding to capture intrinsic geometric structures, and multiview learning to use complementary information from multiple feature spaces. The model assigns intuitionistic fuzzy membership and non-membership values to data points making it robust to outliers. Also, the graph embedding framework preserves topological structures, increasing the generalization performance. We performed experiments on benchmark datasets from UCI and KEEL repositories which concludes that IFGRVFL-MV outperforms existing models in classification accuracy. Our results establish that IFGRVFL-MV is a promising advancement in the domain of uncertainty and multiview environments.