Abstract
Recently, a semantic interpretability index has been proposed to preserve the semantic interpretability of Fuzzy Rule-Based Systems while a tuning of the membership functions is performed. In this work, we extend the proposed multi-objective evolutionary algorithm in order to analyze the performance of the tuning based on this semantic interpretability index while it is combined with a rule selection. To this end, the following three objectives have been considered: error and complexity minimization, and semantic interpretability maximization.
The analyzed method is compared to a single objective algorithm and to the previous approach in two problems showing that many solutions in the Pareto front dominate to those obtained by these methods.
Supported by the Spanish Ministry of Education and Science under grant no. TIN2008-06681-C06-01.
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Gacto, M.J., Alcalá, R., Herrera, F. (2010). Analysis of the Performance of a Semantic Interpretability-Based Tuning and Rule Selection of Fuzzy Rule-Based Systems by Means of a Multi-Objective Evolutionary Algorithm. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_25
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DOI: https://doi.org/10.1007/978-3-642-13025-0_25
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