frbsPMML is a universal framework for representing FRBS models, which is a format adopted from the Predictive Model Markup Language (PMML).
PMML is a format constructed by an XML-based language to provide a standard for describing models produced
by data mining and machine learning algorithms. A main contribution of PMML is
to provide interoperable schemata of predictive models.
Using PMML, we can easily perform these tasks as our models are documented
in an XML-based language. Human experts can also update and modify the model on the files directly.
Since PMML is an XML-based language, the specification is defined by an XML Schema as
recommended by the World Wide Web Consortium (W3C). The PMML format is specified by the main
tag PMML that contains some components. In the following, we describe the main components:
Header: It contains general information about the PMML document,
such as copyright information for the model, its description, application,
and timestamp of generation.
DataDictionary: It contains information related to fields or variables,
such as number, names, types, and value ranges of variables.
MODEL-ELEMENT: It is a main part of the PMML document that consists of models
supported by PMML. In each model, there are several components embedded in the element,
such as MiningSchema and Output.
MiningSchema specifies outlier treatment, a missing value replacement policy,
and missing value treatment, whereas Output shows a description of the output variable.
For example, in a clustering model, we define a schema representing the cluster centers
that are included in the ClusteringModel element.
Besides these components, there are some optional elements, such as MiningBuildTask,
TransformationDictionary, and Extension.
More detailed information about PMML can be found in (Guazzelli et al., 2009).
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.
There are the following benefits offered by frbsPMML, as follows:
Interoperability: It is a standard format for representing many models without
depending on any programming languages (e.g., Java, Python, and C++) and platforms (e.g., Windows, Linux, and Mac).
Tranparency: Since it is formed based on XML Schema containing formal definitions of the available elements, we can
understand FRBS models as written in frbsPMML.
Interpretability: frbsPMML expresses rulebase, database, and inference schema in simple ways. For example,
rulebase is constructed recursively, so that besides it meets to the mathematical logic (predicate), we can
define different operators (i.e., and
and or
) in one rule.
Flexibility: Since frbsPMML is based XML, human experts can easily modify and improve a model in the text file directly.
Reproducibility: Sicen frbsPMML is a universal representation, it allows us to store, share, execute, and reproduce an FRBS model.