Abstract
In the realm of fuzzy systems, interpretability is really appreciated in most applications, but it becomes essential in those cases in which an intensive human-machine interaction is necessary. Accuracy and interpretability are often conflicting goals, thus we used multi-objective fuzzy modeling strategies to look for a good trade-off between them. For assessing interpretability, two different interpretability indexes have been taken into account: Average Fired Rules (AFR), which estimates how simple the comprehension of a specific rule base is, and Logical View Index (LVI), which estimates how much a rule base satisfies logical properties. With the aim of finding possible relationships between AFR and LVI, they have been used in two independent experimental sessions against the classification error. Experimental results have shown that the AFR minimization implies the LVI minimization, while the opposite is not verified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alcalá, R., Ducange, P., Herrera, F., Lazzerini, B., Marcelloni, F.: A multiobjective evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy-rule-based systems. Trans. Fuz. Sys. 17, 1106–1122 (2009), http://dx.doi.org/10.1109/TFUZZ.2009.2023113
Alonso, J.M., Magdalena, L., Cordón, O.: Embedding hilk in a three-objective evolutionary algorithm with the aim of modeling highly interpretable fuzzy rule-based classifiers. In: Fourth Int. Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS). pp. 15–20 (2010)
Alonso, J.M., Magdalena, L., Guillaume, S.: HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism. Int. Journal of Intelligent Systems 23(7), 761–794 (2008)
Deb, K., Pratap, A., Agarwal, S., Meyarevian, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)
Gacto, M., Alcalá, R., Herrera, F.: A double axis classification of interpretability measures for linguistic fuzzy rule-based systems. In: Ninth International Workshop on Fuzzy Logic (accepted) (2011)
Glorennec, P.Y.: Algorithmes d′apprentissage pour systèmes d′inférence floue. Editions Hermès, Paris (1999)
Herrera, F.: Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence 1, 27–46 (2008), http://dx.doi.org/10.1007/s12065-007-0001-5 , 10.1007/s12065-007-0001-5
Mencar, C., Castiello, C., Cannone, R., Fanelli, A.: Interpretability assessment of fuzzy knowledge bases: A cointension based approach. International Journal of Approximate Reasoning 52(4), 501–518 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cannone, R., Alonso, J.M., Magdalena, L. (2011). An Empirical Study on Interpretability Indexes through Multi-objective Evolutionary Algorithms. In: Fanelli, A.M., Pedrycz, W., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2011. Lecture Notes in Computer Science(), vol 6857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23713-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-23713-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23712-6
Online ISBN: 978-3-642-23713-3
eBook Packages: Computer ScienceComputer Science (R0)