Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

2026-07-06

AI Summary

This study addresses the limitations of traditional Random Vector Functional-Link networks (RVFL) in preserving data geometric structure and effectively integrating multi-view information. To overcome these challenges, the authors propose a novel model, IFGRVFL-MV, which, for the first time, incorporates intuitionistic fuzzy sets and graph embedding into the RVFL framework. By jointly modeling uncertainty, topological structure, and multi-view features, the proposed approach significantly enhances classification performance and robustness to outliers. Experimental results on benchmark datasets from UCI and KEEL demonstrate that IFGRVFL-MV consistently outperforms state-of-the-art methods in terms of classification accuracy, thereby validating its effectiveness and technical advancement.

Abstract

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.

Problem

• Random Vector Functional Link
• Multiview Learning
• Geometric Structure Preservation
• Uncertainty Handling
• Feature Views

Innovation

• Intuitionistic Fuzzy Sets
• Graph Embedding
• Multiview Learning
• Random Vector Functional Link
• Uncertainty Handling