Abstract
Ant colony optimization (ACO) techniques have been revealed as an effective way to improve the interpretability of fuzzy models by reformulating an initial model [1,2]. However, despite this reformulation preserves the initial fuzzy rules, new rules can be added in the search for an interpretability enhancement. Thus, differences between the inferences of the initial and final models can arise due to the interaction among the initial rules and their adjacent new rules. This can lead to changes in the accuracy of the initial model. In order to keep the accuracy of the initial fuzzy model, this work proposes to include the difference between the outputs of the initial and final models as an additional criterion to evaluate the optimality of the reformulated model. This will allow to balance the interpretability against the accuracy within the optimization algorithm, even making the interpretability improvement conditional on a strict preservation of the initial accuracy.
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Carmona, P., Castro, J.L., Herrero, J.L. (2012). Balancing Interpretability against Accuracy in Fuzzy Modeling by Means of ACO. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_8
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DOI: https://doi.org/10.1007/978-3-642-31709-5_8
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