Fuzzy Logic Systems with Data Classification - a Cooperative Approach for Intelligent Decision Support

In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets.