徐苏平,杨习贝,于化龙,於东军.一种基于邻域协同表达的分类方法[J].计算机科学,2017,44(9):234-238
一种基于邻域协同表达的分类方法
Neighborhood Collaborative Representation Based Classification Method
投稿时间:2016-08-08  修订日期:2016-11-26
DOI:10.11896/j.issn.1002-137X.2017.09.044
中文关键词:  分类,协同表达,特征选择,邻域,粗糙集
英文关键词:Classification,Collaborative representation,Feature selection,Neighborhood,Rough set
基金项目:本文受国家自然科学基金(61572242,61503160,61305058,61373062),中国博士后科学基金(2014M550293),江苏省普通高校学术学位研究生科研创新计划项目(KYLX16_0505)资助
作者单位E-mail
徐苏平 江苏科技大学计算机科学与工程学院 镇江212003 supingxu@yahoo.com 
杨习贝 江苏科技大学计算机科学与工程学院 镇江212003 zhenjiangyangxibei@163.com 
于化龙 江苏科技大学计算机科学与工程学院 镇江212003 yuhualong@just.edu.cn 
於东军 南京理工大学计算机科学与工程学院 南京210094 njyudj@njust.edu.cn 
摘要点击次数: 208
全文下载次数: 104
中文摘要:
      邻域粗糙集模型中,随着信息粒尺寸的增长,基于多数投票原则的邻域分类器(NC)容易对未知样本的类别产生误判。为了缓解该问题,在协同表达分类(CRC)思想的基础上,提出了一种基于邻域协同表达的分类方法,即邻域协同分类器(NCC)。NCC首先借助邻域粗糙集模型对分类学习任务进行特征选择,然后找出被选特征下未知样本的邻域空间,最后在邻域空间内采用协同表达来代替多数投票原则,找出与未知样本具有最小重构误差的类别作为预测的类别标记。在4组UCI数据集上的实验结果表明:1)与NC相比,所提NCC在大尺寸信息粒下获得了较为满意的分类效果;2)与CRC相比,所提NCC在保持良好分类精度的同时,极大地降低了字典样本的规模,进而提高了分类的效率。
英文摘要:
      In the neighborhood rough set model,with the increasing of the size of information granules,the majority vo-ting rule based neighborhood classifier (NC) is easy to misjudge the classes of unknown samples.To remedy this deficiency,based on the idea of collaborative representation based classification (CRC),we proposed a neighborhood colla-borative representation based classification method,namely,the neighborhood collaborative classifier (NCC).NCC firstly performs feature selection in the classification learning task with neighborhood rough set model,and then finds the neighborhood space of unknown sample under selected features.Finally,instead of the majority voting rule in the neighborhood space,NCC judges the class of unknown sample with the collaborative representation,which considers the class with the minimal reconstruction error for unknown sample as the predicted category.Experimental results on 4 UCI data sets show that compared with NC,the proposed NCC achieves satisfactory performance in larger information granules and compared with CRC,and the proposed NCC greatly reduces the size of the dictionary while maintaining good classification accuracy,and improves the efficiency of classification.
查看全文  查看/发表评论  下载PDF阅读器