Javier Fumanal Idocin

Dynamic feature selection (DFS) offers a compelling alternative to traditional, static feature selection by adapting the selected features to each individual sample. This provides insights into the decision-making process for each case, which makes DFS especially significant in settings where decision transparency is key, i.e., clinical decisions. However, existing DFS methods use opaque models.

Understanding the decisions taken by machine learning systems is instrumental in their deployment in real-world systems, as it enables responsible decision-making, fosters trust, and facilitates debugging and improvement. The research field devoted to studying the techniques that explain and illustrate those decisions is called explainable AI. Fuzzy logic, with its interpretable fuzzy rule-based inference, has emerged as a popular tool for Explainable AI because of these interpretable classifiers. However, current fuzzy logic libraries provide limited inference capabilities and integration to machine learning or are only available in the Java or R language, which makes their integration with the standard machine libraries in Python challenging. This paper describes a software library that contains a Python implementation to perform fuzzy inference using different kinds of fuzzy sets, with a special focus on result visualization. This library follows the scikit-learn programming interface, enabling researchers to utilize it with minimum fuzzy logic background seamlessly. This toolkit unveils novel tools for programming fuzzy systems that are learnable using machine learning methods, leading to data-powered systems that maintain full transparency and accountability, accessible to virtually anyone without specialized AI training. The interpretability of these systems makes them highly valuable in industries like healthcare, law, and security.