摘要
粗糙集理论是一种新的处理模糊和不确定知识的软计算工具。应用粗糙集理论,可以将隐藏在系统的知识能够以决策规则的形式表达出来。根据粗糙集上下近似的概念,决策规则能够分成确定性规则和可能性规则两种。本文将介绍从不完备信息系统中知识获取的算法,通过这些算法能够从不完备决策表中生成一种确定性的规则和两种可能性的规则,同时也介绍了不完备决策表中描述约简的算法。
Rough Set theory is emerging as a powerful tool for reasoning about data. Using Rough Set theory, knowledge hidden in Incomplete Information System may be unraveled and expressed in the form of decision rules. According to the lower and upper approximations, decision rules can be divided into certain and possible rules. Algorithms for knowledge acquisition in incomplete information systems are proposed. As the result, one type of “certain” and two types of “possible” decision rules are generated from incomplete decision tables. Algorithms for reduction of descriptors in such tables are also discussed.
出处
《计算机科学》
CSCD
北大核心
2005年第9期149-152,共4页
Computer Science
基金
国家自然科学基金(60373078)
浙江省教育厅科研计划项目(20040538)资助
