Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a Cluster-Based Class Representation
This work addresses the problem of balancing accuracy and interpretability in classification for domains requiring human-understandable models, but it is incremental as it builds on prior interval type-2 fuzzy set methods.
The paper tackled the challenge of improving classification performance in fuzzy rule-based systems while maintaining interpretability by using subtractive clustering to generate multiple cluster prototypes. The result was comparable performance to non rule-based classifiers like SVM, often with a very small number of rules.
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.