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
施虹艺 1马周明2
作者信息
- 1. 闽南师范大学数学与统计学院,福建 漳州 363000;福建省闽南师范大学粒计算重点实验室,福建 漳州 363000
- 2. 闽南师范大学数学与统计学院,福建 漳州 363000;数字福建气象大数据研究所(闽南师范大学),福建 漳州 363000;福建省闽南师范大学粒计算重点实验室,福建 漳州 363000
- 折叠
摘要
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.
Abstract
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.
关键词
粗糙模糊集/多尺度决策系统/最优尺度选择/回归预测/最小描述Key words
rough fuzzy set/multi-scale decision system/optimal scale selection/regression prediction/minimum description引用本文复制引用
基金项目
国家自然科学基金(11871259)
国家自然科学基金(62076088)
福建省自然科学基金(2021J01979)
福建省自然科学基金(2021J01983)
福建省教育厅中青年教师教育科研项目(JAT220211)
闽南师范大学研究生教育改革资助项目(YJG202209)
出版年
2024
NETL
NSTL
万方数据