基于语义距离的K-最近邻分类方法  

K-Nearest Neighbor Classification Based on Semantic Distance

作　　者：杨立[1] 左春[1] 王裕国[1] 

机构地区：[1]中国科学院软件研究所

出　　处：《软件学报》2005年第12期2054-2062,共9页Journal of Software

基　　金：国家高技术研究发展计划(863);国家"十五"科技攻关计划~~

摘　　要：最近邻分类方法中对距离机制的研究大都集中在根据何种计算方法将不同属性取值的差异集中起来,而未考虑到同一属性间取值的语义差异所带来的影响;而且传统算法的分类准确率对于不同抽象层次描述的数据集带来的数据不完整性相当敏感.针对这两个问题,提出一种基于语义距离的最近邻分类方法SDkNN(semanticdistancebasedk-nearestneighbor).该方法分析了同一属性内取值的语义差异,说明了如何基于领域本体计算语义距离,并将其应用到kNN算法中.经过在UCI数据集以及实际应用数据集中验证,SDkNN的整体性能要优于传统方法,在数据不完整的情况下效果更为明显,实践证明,SDkNN有较好的应用价值.Most research on distance metric of kNN classification is focused on how to integrate the differences caused by various attributes, and the semantic difference between values of the same attribute is ignored. In addition, classification accuracy of the traditional approaches is very sensitive to the incomplete data described on different abstract levels. In this paper, a novel kNN approach based on semantic distance——SDkNN （semantic distance based k-nearest neighbor） is presented, which solves the two problems mentioned above. This approach analyzes the semantic difference between values of an attribute and presents how to calculate the semantic distance based on domain ontologies, and the semantic distance is then used to improve the traditional kNN methods. Experiments on the UCI （University of California, Irvine） machine learning repository and real application datasets show that the overall performance of SDkNN outperforms the traditional one, especially when the data is incomplete. SDkNN also has the desirable application value in practice.

关 键 词：本体 语义距离 最近邻 分类 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]