基于限制非对称相似关系模型的规则获取算法研究

Rule Extraction Algorithm Research Based on Limited Non-symmetric Similarity Relation Model

粗糙集理论在不完备信息系统中的应用,是将粗糙集理论进一步推向实用的关键之一,而经典的粗糙集理论对不完备信息系统的处理显得束手无策.在分析研究已有的扩充粗糙集理论模型的基础上,进一步提出基于限制非对称相似关系模型,并将经典的可辨识关系矩阵加以扩充,定义了限制非对称相似关系下的可辨识关系矩阵,采用布尔推理方法,直接从不完备信息系统中提取规则而无需改变初始不完备信息系统的结构.实验结果表明,所获得的决策规则简洁,与缺省值无关.

The application of rough set theory in incomplete information systems is one of the key problems to study rough set theory in practice. However, It is difficult for classical rough set theory to deal with those incomplete information systems. In this paper, after analyze some kinds of extensions to the classical rough set theory, the concept of limited non-symmetric similarity relation is defined and then classical discernibility matrix is extended based on limited non-symmetric similarity relation. By taking the method of Boolean reasoning, rules are extracted directly from the incomplete decision systems without changing the size of original incomplete systems. The experiment results show that the algorithm provides precise and simple decision rules and does not affected by the missing values.