摘要
布尔序列分类作为一类特殊的分类问题在以往很少被予以专门的研究.本文首先定义布尔序列的属性序化和分片映射的概念,在此基础上提出一种称为序化分片映射(OPM)的降维方法,并将此方法与KNN算法结合提出了一种KNN的改进算法(OPM-KNN).实际数据的实验和分析表明,在降维方面,本文OPM方法与传统PCA方法效果相当,速度有较大提高;在分类方面,本文改进KNN算法与传统的KNN算法相比,分类准确度相当,分类速度增快.
As a special classification problem, classification of Boolean sequences is seldom studied. Definitions of the ordering and piecewise mapping are given. And then a dimension-reduction method called ordering and piecewise mapping （OPM） is put forward. Thus an improved KNN algorithm （OPM-KNN） is presented by integrating OPM with KNN. Analytical and experimental results show the speed of OPM method is improved compared with that of traditional PCA algorithm in dimension reduction. As for classification, the accurate rate of OPM-KNN is almost equivalent to the traditional KNN algorithm or appreciably higher than it and the speed is also faster.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第2期330-336,共7页
Pattern Recognition and Artificial Intelligence
基金
国家973计划资助项目(No.2003CB517102)
关键词
布尔序列
序化
分片映射
降维
K-近邻(KNN)
分类
Boolean Sequence, Ordering, Piecewise Mapping, Dimension Reduction, K-Nearest Neighbor （KNN）, Classification
