基于Pareto协同进化算法的高维模糊分类系统设计

Design of high-dimensional fuzzy classification system based on Pareto co-evolutionary algorithm

提出一种可同时构造多个精确性和解释性较好折衷的高维模糊分类系统的设计方法.该方法首先利用Simba算法进行特征变量选择,然后采用模糊聚类算法辨识初始的模糊模型,最后利用Pareto协同进化算法对所获得的初始模糊模型进行结构和参数优化.其中,Pareto协同进化算法采用了一种新的基于非支配排序的多种群合作策略.为提高模型的解释性,在Pareto协同进化算法中利用基于相似性的模型简化方法对模型进行约简.利用该方法对Wine典型问题进行分类,仿真结果验证了方法的有效性.

A novel approach for constructing accurate and interpretable high-dimensional fuzzy classification systems is proposed. First, feature selection is accomplished by the Simba algorithm; secondly, the initial fuzzy system is identified using the fuzzy clustering algorithm; finally, the structure and parameters of the fuzzy system are optimized by the Pareto co-evolutionary algorithm. The Pareto co-evolutionary algorithm is calculated by a new non-dominated sorting method. In order to improve the interpretability of the fuzzy system, the similarity-driven rule-based simplification techniques are used to reduce the fuzzy system. The proposed approach has been applied to Wine benchmark problem, and the results show its validity.