Hybrid Fuzzy Logic and Machine Learning Models for Predictive Analytics

Abstract

The increasing complexity and uncertainty in modern datasets have made it challenging to develop predictive models that are both highly accurate and interpretable. While powerful in data processing and prediction accuracy, traditional machine learning (ML) models often need help dealing with imprecise, incomplete, or uncertain data. These models may need help to capture the inherent vagueness present in real-world scenarios. On the other hand, fuzzy logic systems, designed to handle uncertainty and imprecision, excel in modeling such uncertain information but lack the adaptive learning capabilities that machine learning provides. In recent years, integrating fuzzy logic with machine learning has emerged as a promising approach to overcome these limitations. This paper explores the synergy between fuzzy logic systems and machine learning techniques in creating hybrid models for predictive analytics. Combining both methodologies' strengths, hybrid systems can provide more robust, accurate, and interpretable models. These integrated approaches offer significant advantages in tackling complex, uncertain problems across various domains such as finance, healthcare, and manufacturing. The paper discusses these hybrid models' potential applications and benefits, illustrating their ability to improve decision-making and forecasting in real-world environments.