Multi-objective evolutionary algorithm for tuning the Type-2 inference engine on classification task
- 33 Downloads
Type-2 fuzzy systems have been investigated as an alternative formalism to deal with uncertainty when the classic Type-1 fuzzy systems do not offer the suitable flexibility for the representation of the information being modeled. The higher flexibility in representation comes with a higher complexity in the system modeling, mainly in the design of the Type-2 fuzzy sets and in the definition of the inference engine parameters. In this paper, we focus on the Type-2 fuzzy systems design, proposing a multi-objective evolutionary approach for tuning the Type-2 inference engine of a fuzzy rule-based classification system by means of automatically choosing the t-norm used in the inference process. The selection of the t-norm used plays an important hole, since different operators could lead to different results. In a preliminary version of this work, we have proposed an approach to design and optimize Type-2 fuzzy systems that includes the tuning of Type-2 fuzzy sets and the selection of rules. The additional tuning process proposed in this paper is an extension of the previous method in the sense that the same evolutionary procedure performs simultaneously the tuning of the inference mechanism and the tasks performed before. The evolutionary process is executed by means of a multi-objective genetic algorithm with three objectives that aim to balance the accuracy and interpretability of the system generated: the accuracy, the number of rules and the number of conditions in the rules. The proposed method has been compared with a state-of-the-art method proposed in the literature, presenting good results.
KeywordsType-2 fuzzy inference system Fuzzy rule-based classification systems Tuning Multi-objective evolutionary algorithms
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult-Valued Log Soft Comput 17(2–3):255–287Google Scholar
- Cai A, Quek C, Maskell DL (2007) Type-2 GA-TSK fuzzy neural network. In: 2007 IEEE Congress on evolutionary computation, pp 1578–1585. https://doi.org/10.1109/CEC.2007.4424661
- Chua TW, Tan WW (2008) Genetically evolved fuzzy rule-based classifiers and application to automotive classification. In: Simulated evolution and learning. Springer, Berlin, pp 101–110Google Scholar
- Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. In: Advances in fuzzy systems—applications and theory, vol 19. World Scientific Publishing Co. Pte. Ltd, SingaporeGoogle Scholar
- Hinojosa E, Carmago HA (2012) Multiobjective genetic optimization of fuzzy partitions and t-norm parameters in fuzzy classifiers. In: 2012 Brazilian symposium on neural networks, pp 154–159. https://doi.org/10.1109/SBRN.2012.45
- Hinojosa CE, Camargo HA (2018) A multi-objective evolutionary algorithm for tuning Type-2 fuzzy sets with rule and condition selection on fuzzy rule-based classification system. Springer, Berlin pp 389–399Google Scholar
- Karnik NN, Mendel JM (1998) Introduction to type-2 fuzzy logic systems. In: 1998 IEEE international conference on fuzzy systems proceedings. In: IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), vol 2, pp 915–920Google Scholar
- Lucca G, Dimuro GP, Mattos V, Bedregal B, Bustince H, Sanz JA (2015) A family of Choquet-based non-associative aggregation functions for application in fuzzy rule-based classification systems. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–8Google Scholar
- Martinez SZ, Coello CAC (2014) A multi-objective evolutionary algorithm based on decomposition for constrained multi-objective optimization. In: 2014 IEEE Congress on evolutionary computation (CEC), pp 429–436Google Scholar
- Trk S, John R, Özcan E (2014) Interval type-2 fuzzy sets in supplier selection. In: 2014 14th UK workshop on computational intelligence (UKCI), pp 1–7Google Scholar
- Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm, Technical reportGoogle Scholar