摘要
传统的KNN算法存在分类效率低等缺点.针对这些缺点,本文提出一种高效的结合多代表点思想的加权KNN算法,利用变精度粗糙集上下近似区域的概念,结合聚类算法生成代表点集合构造分类模型,再运用结构风险最小化理论优化分类模型并对影响分类模型的因素进行分析.分类过程中根据测试样本与各代表点的相似度,得到测试样本的相对位置.其中属于样本点下近似区域的测试样本可直接判断其类别.若测试样本在其他区域,则根据测试样本与各代表点的相对位置对各代表点覆盖范围内的样本进行加权后判断测试样本的类别.在文本分类领域的数据集上进行实验,结果表明该算法能有效的提高分类模型的性能.
The traditional KNN algorithm has shortcomings such as low classification efficiency.This study proposes an efficient weighted KNN algorithm that combines the idea of multiple representative points.It uses the concept of the upper and lower approximate regions of the variable precision rough set and integrates the clustering algorithm to generate a representative point set and construct a classification model.Then it adopts the structural risk minimization theory to optimize the classification model and analyze the factors that affect the classification model.During the classification process,the relative position of the test sample is obtained according to the similarity between the test sample and each representative point.Moreover,the category of the test sample in the lower approximate region can be directly determined.If the test sample is in other areas,the sample within the coverage of each representative point is weighted according to the relative position of the test sample and each representative point to determine the type of the test sample.Experiments on the data set in the field of text classification show that the algorithm can improve the performance of the classification model.
作者
林高思源
LIN Gao-Si-Yuan(College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China)
出处
《计算机系统应用》
2021年第12期273-278,共6页
Computer Systems & Applications
关键词
近邻分类
文本分类
变精度粗糙集
代表点
分类模型
样本加权
nearest neighbor classification
text classification
variable precision rough set
representative
classification model
sample weighting
作者简介
通讯作者:林高思源,E-mail:sylingao@gmail.com。
