作　　者： ; ;

机构地区： 广东石油化工学院

出　　处： 《计算机应用》 2011年第7期1785-1788,1792,共5页

摘　　要： 特征参数分类泛化性差及分类计算量大影响着K近邻(KNN)的分类性能。提出了一种降维条件下基于联合熵的改进KNN算法,其具体思路是,通过计算任意两个条件属性下对应的特征参数的联合熵衡量数据特征针对分类影响程度的大小,建立特征分类特性与具体分类过程的内在联系,并给出根据特征联合熵集约简条件属性的方法。理论分析与仿真实验表明,与经典KNN等算法相比,提出的算法具有更高的分类性能。 Poor generalization of feature parameters classification and large category computation reduce the classification performace of K-Nearest Neighbor（KNN）.An improved KNN based on union entropy under the attribute reduction condition was proposed.Firstly,the size of classification impact of data feature was measured by calculating the union entropy of two feature parameters relative to any two condition attributes,and the intrinsic relation was established between classified features and the specific classification process.Then,the method which reduced condition attributes according feature union entropy set was given.The theoretical analysis and the simulation experiment show that compared with the classical KNN,the improved algorithm has better classification performance.

关 键 词： 近邻 特征 联合熵 条件属性 分类

领　　域： [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术]