摘要
粒计算是模拟人类思维和解决复杂问题的方法,它是复杂问题求解、海量数据挖掘、模糊信息处理的有效工具.文中首先分析并指出传统的规则获取方法存在的某些弊端,并从粒计算的角度分析属性约简的粒度原理,指出属性约简过程的本质是寻找决策划分空间的一个极大近似划分空间,而在极大近似划分空间上提取的规则可能不是最简规则.为此,提出一种基于最大粒的规则获取算法,该算法根据条件属性对论域形成的分层递阶的划分空间,自顶向下逐渐提取最大粒对应的规则.仿真实验表明该算法提高粗糙集的泛化能力.
Granular computing （GrC） is a method for simulating human thinking and solving complicated problems. It is a powerful tool for solving complicated problems, mining massive data sets, and dealing with fuzzy information. In this paper, the shortcoming of the traditional rule extraction methods is presented, and then the granularity principle of rule extraction is analyzed based on granular computing method. The essence of attribute reduction is to choose a maximum approximation partition space of decision-making knowledge space, and the rules acquired from maximum approximation partition space may not be the simplest. Therefore, a rule extraction algorithm based on granular computing is proposed. In the proposed algorithm, the rules based on maximal granule can be acquired from information system in a hierarchical knowledge space in top-down manner, and the results of the simulation experiments illustrate that the generalization ability of rough set method is improved.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2012年第3期388-396,共9页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61073146
61075019)
重庆市教育科学研究项目(No.KJ110512)资助
关键词
属性约简
知识获取
粒计算
知识空间
商空间
Attribution Reduction, Knowledge Acquisition, Granular Computing, Knowledge Space,Quotient Space
