Applications of IFIS python library in interval-valued fuzzy inference problems

In many data domains, such as engineering and medical diagnostics, the inherent uncertainty within datasets is a critical factor that must be addressed during decision-making processes. To effectively manage this uncertainty, it is crucial to utilize methodologies that can accurately incorporate it. The theory of interval-valued fuzzy sets, particularly the interval model of inference, commonly known as generalized fuzzy inference, has demonstrated significant effectiveness in handling these uncertainties. This paper introduces an innovative application of the Interval-Valued Fuzzy Inference System (IFIS) Library. This library is an enhancement of the Simpful library, a widely accessible and easy-to-use Python library for the creation, analysis, and interpretation of fuzzy inference systems. Our enhancement significantly augments the library's capabilities, enabling the straightforward definition of interval-valued fuzzy sets and rules, along with the execution of generalized fuzzy inference. Through practical examples, we illustrate the utility and added value of our library extension, establishing it as a significant contribution to the array of open-source software available for supporting generalized fuzzy inference in various application domains.