Attributes regrouping by genetic algorithm in fuzzy inference systems
Maroua Lejmi, Ilef Ben Slima, Amel Borgi · Procedia Computer Science · 2022
Fuzzy Rule-Based Classification Systems (FRBCS) are a very popular and useful tool due to their interpretable classification models based on linguistic variables. However, a high number of attributes leads to an explosion of the number of classification rules. In this paper, we are interested in ensemble methods applied to FRBCS. An attributes regrouping method based on association rules and called SIFRA was proposed to solve this problem. It determines the relevant groups of related attributes that will be treated separately by FRBCSs. We propose a Genetic Algorithm-based method to select final groups of related attributes determined by SIFRA. In our approach, the genetic algorithm is used to filter the groups of related attributes and conserve the best ones, while maintaining a good classification rate and a reduced number of rules. We lead experiments to compare our contribution with previous methods in the same context; the experimental results are satisfactory.