Geração genética multiobjetivo de sistemas fuzzy usando a abordagem iterativa
Cárdenas, Edward Hinojosa
MetadataShow full item record
The goal of this work is to study, expand and evaluate the use of multiobjective genetic algorithms and the iterative rule learning approach in fuzzy system generation, especially, in fuzzy rule-based systems, both in automatic fuzzy rule generation from datasets and in fuzzy sets optimization. This work investigates the use of multi-objective genetic algorithms with a focus on the trade-off between accuracy and interpretability, considered contradictory objectives in the representation of fuzzy systems. With this purpose, we propose and implement an evolutive multi-objective genetic model composed of three stages. In the first stage uniformly distributed fuzzy sets are created. In the second stage, the rule base is generated by using an iterative rule learning approach and a multiobjective genetic algorithm. Finally the fuzzy sets created in the first stage are optimized through a multi-objective genetic algorithm. The proposed model was evaluated with a number of benchmark datasets and the results were compared to three other methods found in the literature. The results obtained with the optimization of the fuzzy sets were compared to the result of another fuzzy set optimizer found in the literature. Statistical comparison methods usually applied in similar context show that the proposed method has an improved classification rate and interpretability in comparison with the other methods.
Showing items related by title, author, creator and subject.
Processamento de conhecimento impreciso combinando raciocínio de ontologias fuzzy e sistemas de inferência fuzzy Yaguinuma, Cristiane Akemi (Universidade Federal de São Carlos, UFSCar, Programa de Pós-Graduação em Ciência da Computação - PPGCC, , 13/12/2013)In Computer Science, ontologies are used for knowledge representation in a number of applications, aiming to structure and handle domain semantics through models shared by humans and computational systems. Although traditional ...
Pimenta, Adinovam Henriques de Macedo (Universidade Federal de São Carlos, UFSCar, Programa de Pós-Graduação em Ciência da Computação - PPGCC, , 30/10/2009)The objective of this work is to study, expand and evaluate the use of interval type-2 fuzzy sets in the knowledge representation for fuzzy inference systems, specifically for fuzzy classifiers, as well as its automatic ...
Lopes, Mariana Vieira Ribeiro (Universidade Federal de São Carlos, UFSCar, Programa de Pós-Graduação em Ciência da Computação - PPGCC, Câmpus São Carlos, 03/03/2016)Inductive Decision Trees (DT) are mechanisms based on the symbolic paradigm of machine learning which main characteristics are easy interpretability and low computational cost. Though they are widely used, the DTs can ...