[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83986-en":3,"doc-seo-83986-105":28,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},83986,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning","Random Vector Functional Link (RVFL) networks are valued for fast training and universal approximation, yet they struggle to preserve geometric relationships and to exploit multiple feature views effectively. The IFGRVFL-MV model integrates intuitionistic fuzzy sets for uncertainty and outlier robustness, graph embedding to retain intrinsic topological/geometric structure, and multiview learning to combine complementary information from different feature spaces. Experiments on UCI and KEEL benchmarks show improved classification accuracy over existing methods.","Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning  \nVrushank Ahire Yogesh Kumar M.A. Ganaie  \n[2022csb1002@iitrpr.ac.in](2022csb1002@iitrpr.ac.in) [yogesh.23csz0014@iitrpr.ac.in](yogesh.23csz0014@iitrpr.ac.in) [mudasir@iitrpr.ac.in](mudasir@iitrpr.ac.in)  \nDept. of Computer Science and Engineering, IIT Ropar, Punjab, India  \narXiv :2607 .05635v 1 [ cs .LG] 6 Jul 2026  \nAbstract—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 nonmembership 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.  \nIndex Terms—Intuitionistic Fuzzy Logic, Graph Embedding, RVFL, Multiview Learning  \nI. INTRODUCTION  \nMachine learning has evolved significantly over the years, with traditional algorithms such as Support Vector Machines (SVMs) [1], Decision Trees, and k-Nearest Neighbors (kNN) being widely used for classification and regression tasks. However, these methods often face challenges such as high computational complexity, sensitivity to hyperparameters, and difficulties in handling noisy or uncertain data [2] . For instance, SVMs, while effective for binary classification, struggle with large-scale datasets due to their quadratic optimization complexity [3] . Similarly, traditional neural networks trained using backpropagation are prone to issues like slow convergence, local minima, and overfitting [4] .  \nIn contrast, Randomized Neural Networks (RNNs), particularly the RVFL network, have emerged as a promising alternative due to their simplicity, fast training speed, and universal approximation capabilities [5] . Unlike traditional neural networks, RVFL networks randomly initialize the weights of the hidden layer and compute the output weights analytically, avoiding the need for iterative optimization [6] . This approach not only reduces training time but also eliminates the risk of getting stuck in local minima. Furthermore, the direct links between the input and output layers in RVFL networks enhance their generalization performance, making them suitable  \nfor a wide range of applications, including healthcare, image classification and forecasting [7] .  \nRecent research has focused on enhancing the capabilities of RVFL networks by incorporating advanced techniques such as fuzzy logic, Graph Embedding (GE), and Multiview Learning (MVL) . For instance, the Intuitionistic Fuzzy RVFL (IFRVFL) model integrates intuitionistic fuzzy sets to handle uncertainty and imprecision in data, assigning membership and nonmembership values to each sample based on its distance from the class center and the heterogeneity of its neighbors [8] . This approach improves the model’s robustness to noise and outliers, making it particularly effective for noisy datasets. Similarly, the Graph Embedded RVFL (GE-RVFL) model utilizes the GE framework to capture the geometric relationships within the data, preserving the topological structure and enhancing generalization performance [9] . ","cbCain0GiCuNQgiM","https://ap.wps.com/l/cbCain0GiCuNQgiM","pdf",380841,1,"English","en",105,"# Introduction\n## Motivation and background\n## Related work on RVFL, fuzzy logic, and multiview learning\n## Identified gaps and proposed direction","[{\"question\":\"What problem do RVFL models face that IFGRVFL-MV targets?\",\"answer\":\"RVFL models may not preserve geometric relationships well and may not effectively use multiple feature views, which can hurt performance on noisy or uncertain data.\"},{\"question\":\"How does IFGRVFL-MV handle uncertainty and outliers?\",\"answer\":\"It assigns intuitionistic fuzzy membership and nonmembership values to data points, making the learning process robust to outliers.\"},{\"question\":\"How do graph embedding and multiview learning contribute to the model?\",\"answer\":\"Graph embedding preserves intrinsic topological/geometric structures for better generalization, while multiview learning leverages complementary information from multiple feature spaces to improve classification 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