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In this paper, we propose a three-objective evolutionary algorithm to generate a set of Mamdani fuzzy rule-based systems (MFRBSs) with different tradeoffs between accuracy, rule base (RB) complexity and partition integrity. The RB, the linguistic partition granularities and the membership function (MF) parameters are concurrently learnt during the evolutionary process. In particular, the granularity...
When applied to high dimensional datasets, multi-objective evolutionary learning (MOEL) of fuzzy rule-based systems suffers from high computational costs, mainly due to the fitness evaluation. To use a reduced training set (TS) in place of the overall TS could considerably lessen the required effort. How this reduction should be performed, especially in the context of regression, is still an open...
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...
In this paper we propose a multi-objective genetic algorithm to generate Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we exploit a chromosome composed of two parts, which codify...
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