A Universal Representation Framework for Fuzzy Rule-Based Systems Based on PMML

Fuzzy rule-based systems (FRBSs) have been implemented and deployed by researchers and practitioners in many different application contexts to deal with complex real-world problems. However, a challenge that still remains mainly unresolved is the lack of a general representation framework for FRBSs that allows interoperability among platforms and applications. Therefore, this paper proposes a universal framework for representing FRBS models, called frbsPMML, which is a format adopted from the Predictive Model Markup Language (PMML). Three models, which can be used for handling regression and classification tasks, are specified by the proposed representations: Mamdani, Takagi Sugeno Kang, and fuzzy rule-based classification systems. A key advantage of FRBS model specification in frbsPMML is that high degrees of transparency and interpretability can be achieved. Moreover, an easier deployment and integration of FRBSs with other tools for modelling and data analysis becomes possible, as well as easier reproducibility of research. In this paper we also present two implementations of the proposed standard model format: the R package “frbs” as an frbsPMML producer and consumer, and a Java implementation of an frbsPMML consumer, named “frbsJpmml.”A comparison with other representations and examples to show schemata of the frbsPMML format are provided.