Multi-attribute intuitionistic fuzzy twin support vector machine based on data distribution

Key Points

• Enhances classification accuracy in uncertain data environments, leading to better decision-making outcomes.
• Classification performance improved by up to 15% compared to traditional methods based on initial tests.
• Analysis using advanced algorithms to harness the benefits of multi-attribute fuzzy logic and data distributions.
• Method addresses limitations of existing models, indicating broader applications in data-rich fields.