Vrushank Ahire

Vrushank Ahire, Yogesh Kumar, M. A. Ganaie

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.

M. A. Ganaie, Vrushank Ahire, Anouck Girard

This paper introduces the Granular Ball K-Class Twin Support Vector Classifier (GB-TWKSVC), a novel multi-class classification framework that combines Twin Support Vector Machines (TWSVM) with granular ball computing. The proposed method addresses key challenges in multi-class classification by utilizing granular ball representation for improved noise robustness and TWSVM's non-parallel hyperplane architecture solves two smaller quadratic programming problems, enhancing efficiency. Our approach introduces a novel formulation that effectively handles multi-class scenarios, advancing traditional binary classification methods. Experimental evaluation on diverse benchmark datasets shows that GB-TWKSVC significantly outperforms current state-of-the-art classifiers in both accuracy and computational performance. The method's effectiveness is validated through comprehensive statistical tests and complexity analysis. Our work advances classification algorithms by providing a mathematically sound framework that addresses the scalability and robustness needs of modern machine learning applications. The results demonstrate GB-TWKSVC's broad applicability across domains including pattern recognition, fault diagnosis, and large-scale data analytics, establishing it as a valuable addition to the classification algorithm landscape.

M. A. Ganaie, Vrushank Ahire

Classification with support vector machines (SVM) often suffers from limited performance when relying solely on labeled data from target classes and is sensitive to noise and outliers. Incorporating prior knowledge from Universum data and more robust data representations can enhance accuracy and efficiency. Motivated by these findings, we propose a novel Granular Ball Twin Support Vector Machine with Universum Data (GBU-TSVM) that extends the TSVM framework to leverage both Universum samples and granular ball computing during model training. Unlike existing TSVM methods, the proposed GBU-TSVM represents data instances as hyper-balls rather than points in the feature space. This innovative approach improves the model's robustness and efficiency, particularly in handling noisy and large datasets. By grouping data points into granular balls, the model achieves superior computational efficiency, increased noise resistance, and enhanced interpretability. Additionally, the inclusion of Universum data, which consists of samples that are not strictly from the target classes, further refines the classification boundaries. This integration enriches the model with contextual information, refining classification boundaries and boosting overall accuracy. Experimental results on UCI benchmark datasets demonstrate that the GBU-TSVM outperforms existing TSVM models in both accuracy and computational efficiency. These findings highlight the potential of the GBU-TSVM model in setting a new standard in data representation and classification.