Attribute Weights Mining for Case-Intelligent System Reasoning on Similarity Rough Sets

2018 3rd International Conference on Insulating Materials, Material Application and Electrical Engineering

材料科学;无线电电子学;电工学

Li, Jianyang^1,2 ; Song, Changtong^2 ; Wang, Qi^2

School of Electrical Engineering, Zhenjiang Institute, Zhenjiang, China^1

School of Computer Engineering, Hefei University of Technology, Hefei, China^2

关键词: Attributes reduction;    CBR (case based reasoning);    Creative thinking;    Decision strategy;    Empirical calculations;    Machine learning methods;    Real-world problem;    Reasoning models;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/452/4/042110/pdf
DOI  :  10.1088/1757-899X/452/4/042110

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Attribute Weights Assignment is an important method to solve real world problems full of uncertainty, for it is difficult to acquire a comprehensive formula theoretically with which so many empirical calculations have to be drilled out of domain experts. Case-Intelligent System based on CBR (case-based reasoning), which is a human creative thinking and useful reasoning model for problem-solving, can acquire prior knowledge from the former stored cases implicating decision strategy empirical and powerful, and construct a flexible system integrated with efficient machine learning methods coping with uncertainty. Attribute weights also are the key for case similarity measurement and optimal case selection in CBR cycle, so the similarity Rough Sets is proposed for case attributes reduction, knowledge obtainment and objective weights acquiring in our case-intelligent system, which performs well in real experiments on decision-making and achieves reasonable explanations.