【摘要】 提出一种基于半监督学习的粗糙集知识约简算法(SLRS).SLRS基于对信息论基本概念的引申定义,描述了各条件属性的重要程度以及相互之间的依赖关系.对于数据库中某些记录属性域存在的缺失值,基于半监督学习进行启发式属性值约简,进而求取粗糙集约简决策表,即使在现有知识不足或信息不完备的情况下,也能通过半监督学习构造新的规则补充到知识库中.样例分析及在UCI数据集上的实验结果均表明了所提出算法的合理性和有效性.更多还原

【Abstract】 A rough sets approach named semi-supervised learning rough set(SLRS) is proposed based on semi-supervised learning.Some extended definitions of information theory,such as conditional mutual information between relative values are used to measure the attribute importance and relevancy in a single sample.And then attribute values are reduced heuristically by applying semi-supervised learning.The reduced decision table can be obtained.Even when processing incomplete information systems or lacking prior knowledge,the proposed rules can be learned and added to the knowledge base.The experimental results on UCI machine learning data sets and analysis of the instances show the reasonability and effectiveness of the proposed algorithm.更多还原