Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a
  Cluster-Based Class Representation
  Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to
provide so-called interpretable classifiers, i.e. classifiers which can be
introspective, understood, validated and augmented by human experts by relying
on fuzzy-set based rules. This paper builds on prior work for interval type-2
fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are
generated using an initial clustering stage. By introducing Subtractive
Clustering in order to identify multiple cluster prototypes, the proposed
approach has the potential to deliver improved classification performance while
maintaining good interpretability, i.e. without resulting in an excessive
number of rules. The paper provides a detailed overview of the proposed FRBC
framework, followed by a series of exploratory experiments on both linearly and
non-linearly separable datasets, comparing results to existing rule-based and
SVM approaches. Overall, initial results indicate that the approach enables
comparable classification performance to non rule-based classifiers such as
SVM, while often achieving this with a very small number of rules.
