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

Abstract

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.

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

“…In Table 7, Roubos' algorithm [1], TSFR [2], SC-BPN [3], DDFC [4], GA-BPN [5], FRS-OC-GA [6], HFRBCS [7], Zhou [8]'s algorithm, and FOF [14], populate the following columns. Compared with other classification experiments, our method gives the best classification rate for three of the five databases.…”

“…Data-driven fuzzy classifier (DDFC) [4] utilized co-evolutionary methodology is applied to design the fuzzy classifier. The GA-BPN [5] utilized evolutionary algorithm, the FRS-OC-GA [6] based on compact fuzzy rule system with GA, hierarchical fuzzy rule based classification systems with genetic rule selection (HFRBCS) [7], and Zhou and Khotanzard's fuzzy-rule-based classifier using genetic algorithms [8] are all proposed to improve the categorization accuracy through evolutionary algorithm.…”