Granular Ball K-Class Twin Support Vector Classifier

• URL: http://arxiv.org/abs/2412.05438v1
• Date: Fri, 06 Dec 2024 21:47:49 GMT
• Title: Granular Ball K-Class Twin Support Vector Classifier
• Authors: M. A. Ganaie, Vrushank Ahire, Anouck Girard,
• Abstract summary: Granular Ball K-Class Twin Support Vector (GB-TWKSVC) GB-TWKSVC is a novel multi-class classification framework that combines Twin Support Vector Machines with granular ball computing. Results demonstrate GB-TWKSVC's broad applicability across domains including pattern recognition, fault diagnosis, and large-scale data analytics.
• Score: 5.543867614999908
• License:
• Abstract: 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.

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