Designing a compact Genetic fuzzy rule-based system for one-class classification

This paper proposes a method for designing Fuzzy Rule-Based Classification Systems to deal with One-Class Classification, where during the training phase we have access only to objects originating from a single class. However, the trained model must be prepared to deal with new, unseen adversarial objects, known as outliers. We use a Genetic Algorithm for learning the granularity, domains and fuzzy partitions of the model and we propose an ad-hoc rule generation method specific for One-Class Classification. Several datasets from UCI repository, previously transformed to one-class problems, are used in the experiments and we compare with two of the classical methods used in the One-Class community, one-class Support Vector Machines and Support Vector Data Description. Our proposal of fuzzy model obtains similar results than the other methods but presents a high interpretability due its reduced number of rules.