摘要
由于制造系统的复杂性和不确定性,单一的知识建模或数据挖掘建模都面临着知识或数据信息的不完备.为有效、充分地利用已有信息减少不确定性,文中提出了知识和数据挖掘相融合的建模思想,将知识嵌入到粗糙集模型中,建立了知识的函数关系,给出了基于不可分辨-函数关系的粗糙集决策模型,研究了不可分辨-函数关系下的知识分类和推理.相比原粗糙集模型,基于知识的粗糙集模型具有更高的划分精度,发现知识更丰富,结构形式更具归纳性.实验结果验证了决策模型的有效性和应用的灵活性.
Due to the complexity and uncertainties of the manufacturing system, both the knowledge modeling and the data mining modeling are restricted by incomplete knowledge and data. In this paper, in order to reduce the uncertainties by making full and effective use of the known information, an modeling idea of integrating the knowledge with the data mining is proposed to embed the knowledge into the rough set model, with the function relations of the knowledge established. Then, based on the indiscernibility relations and the function relations, a novel knowledge-based rough set model （KBRSM） is developed and the knowledge classification and inference are investigated. As compared with the original rough set model, KBRSM is of high classification accuracy, excellent performance of knowledge discovery as well as of a generalized form. Experimental results show that the proposed decision model is effective and practically flexible.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第8期36-41,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
国家"863"计划项目(2009AA043901)
关键词
决策
知识融合
粗糙集
不可分辨-函数关系
decision
knowledge fusion
rough set
indiscernibility-function relation
