多尺度决策系统的覆盖粗糙模糊集及其最优尺度选择

Covering rough fuzzy sets and optimal scale selection in multi⁃scale decision systems

进一步推广基于覆盖的粗糙模糊集模型,在对象最小描述的近邻域上考虑对象在决策属性下的隶属度,提出了2种不同的覆盖粗糙模糊集,将覆盖粗糙模糊集与多尺度决策系统结合,构建了4种多尺度决策系统中的覆盖粗糙集模型。定义了对应的正域和属性重要度,设计相应的最优尺度选择算法。最后通过实验分析,比较了4种覆盖粗糙模糊集模型在多尺度决策系统中的最优尺度与原始尺度在回归预测效果上的差异。实验结果表明,第4种多尺度决策系统覆盖粗糙模糊集模型所选择的最优尺度组合有效提高回归模型的预测能力。

This paper extends the covering rough fuzzy set model by considering the membership degree of objects in the decision at⁃tribute in the neighborhood of the objects minimum description.Two different covering rough fuzzy set are proposed.Based on this,four covering rough set models in multi⁃scale decision systems are constructed by combining covering rough fuzzy sets with multi⁃scale decision systems.The corresponding positive region and attribute importance are defined,and the optimal scale selection algo⁃rithm is designed.Finally,comparative experiments compare the difference in regression prediction performance between the optimal scale selected by the four covering rough fuzzy set models in such multi⁃scale decision systems and the original scale.The experi⁃mental results indicate that the optimal scale combination selected by model four of covering rough fuzzy sets in multi⁃scale decision systems can effectively improve the predictive ability of the regression model.