Harnessing Uncertainty: Integrating Fuzzy Logic into Machine Learning Algorithms
Rahib Imamguluyev, Shaban Beshirov Hashim, Ilham Hajiyev · 2024
As machine learning algorithms continue to advance, the incorporation of uncertainty modeling becomes pivotal for robust and adaptable systems. This article explores the fusion of fuzzy logic with machine learning methodologies, presenting a comprehensive approach to harnessing uncertainty in data-driven decision-making processes. The integration of fuzzy logic provides a nuanced framework that accommodates imprecise and ambiguous information, enhancing the algorithms’ capacity to handle real-world complexities. Through a detailed examination of the synergies between fuzzy logic and machine learning, this study contributes to the development of more resilient and versatile systems, demonstrating the efficacy of uncertainty-aware models in various applications. The findings underscore the potential for improved accuracy and interpretability in machine learning outcomes by embracing the inherent uncertainty in data.