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A novel approach to construct accurate and interpretable fuzzy classification system based on hybrid Co-evolution algorithm is proposed in this paper. The approach is composed of three phases: (1) the initial fuzzy system is identified using the Simba algorithm and the fuzzy clustering algorithm; (2) the fuzzy rule pool is optimized by the Michigan-style genetic algorithm; (3) the structure and parameters...
A novel approach to construct accurate and interpretable fuzzy classification system based on hybrid co-evolution algorithm is proposed in this paper. First, the necessary conditions of interpretability are analyzed. Second, the search ability of Michigan-style and Pittsburgh-style genetic algorithm is examined respectively for designing fuzzy classification system. It is clearly demonstrated that...
An approach to construct interpretable and precise fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. Then a modified fuzzy clustering algorithm, combined with the least square method, is used to identify the initial fuzzy model. Third, the multi-objective genetic algorithm and interpretability-driven simplification...
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