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EnglishIn this paper, we introduce a new index for evaluating the interpretability of Mamdani fuzzy rule-based systems (MFRBSs). The index takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in the multi-objective evolutionary generation of MFRBSs with different trade-offs between accuracy and interpretability. The rule base and the membership function parameters of the MFRBSs are learnt concurrently by exploiting an appropriate chromosome coding and purposely-defined genetic operators. Results on a real-world regression problem are shown and discussed.
| Titolo: | Exploiting a New Interpretability Index in the Multi-Objective Evolutionary Learning of Mamdani Fuzzy Rule-Based Systems | |
| Autori interni: | ||
| Anno del prodotto: | 2009 | |
| Abstract: | In this paper, we introduce a new index for evaluating the interpretability of Mamdani fuzzy rule-based systems (MFRBSs). The index takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in the multi-objective evolutionary generation of MFRBSs with different trade-offs between accuracy and interpretability. The rule base and the membership function parameters of the MFRBSs are learnt concurrently by exploiting an appropriate chromosome coding and purposely-defined genetic operators. Results on a real-world regression problem are shown and discussed. | |
| Handle: | http://hdl.handle.net/11568/200548 | |
| ISBN: | 9780769538723 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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