Evolving fuzzy rule-based classifier based on GENEFIS

This paper presents a novel evolving fuzzy rule-based classifier stemming from our recently developed algorithm for regression problem termed generic evolving neuro-fuzzy system (GENEFIS). On the one hand, the novel classifier namely GENEFIS-class is composed of two different architectures specifically zero and first orders which are dependent on the type of consequent used. On the other hand, GENEFIS-class refurbishes GENEFIS algorithm as the main learning engine to conform classification requirement. The interesting property of GENEFIS is its fully flexible rule base and its computationally efficient algorithm. GENEFIS can initiate its learning process from scratch with an empty rule base and highly narrow expert knowledge. The fuzzy rules are then flourished based on the novelty of streaming data via their statistical contribution. Conversely, the fuzzy rules, which contribute little during their lifespan, can be pruned by virtue of their contributions up to the end of training process. Meanwhile, the fuzzy rules and fuzzy sets, which are redundant, can be merged to purpose a transparent rule base. Online feature selection process coupled during the training process can be undertaken to cope with possible combinatorial rule explosion drawback. All of these are fruitful to grant significant reduction of rule base load while retaining the classification accuracy which is in line with online real-time necessity. The efficacy of GENEFIS-class was numerically validated exploiting real world and synthetic problems and compared with state-of-the-art algorithms where it generally speaking outperforms other algorithms in terms of classification performance and rule-base complexity.