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[1]付康安,郭虎升,王文剑*. 基于关联关系分析的符号数据分类方法[J].南京大学学报(自然科学),2017,53(4):815.[doi:10.13232/j.cnki.jnju.2017.04.025]
 Fu Kangan,Guo Husheng,Wang Wenjian*. Categorical data classification approach based on correlation analysis[J].Journal of Nanjing University(Natural Sciences),2017,53(4):815.[doi:10.13232/j.cnki.jnju.2017.04.025]
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 基于关联关系分析的符号数据分类方法()
     

《南京大学学报(自然科学)》[ISSN:0469-5097/CN:32-1169/N]

卷:
53
期数:
2017年第4期
页码:
815
栏目:
出版日期:
2017-08-03

文章信息/Info

Title:
 Categorical data classification approach based on correlation analysis
作者:
 付康安1郭虎升1王文剑12*
 1.山西大学计算机与信息技术学院,太原,030006;
2.山西大学计算智能与中文信息处理教育部重点实验室,太原,030006
Author(s):
 Fu Kang’an1Guo Husheng1Wang Wenjian12*
 1.School of Computer and Information Technology,Shanxi University,Taiyuan,030006,China;
2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan,030006,China
关键词:
 符号属性数据关联关系分析支持向量机分类方法
Keywords:
 categorical datacorrelation analysissupport vector machineclassificiation approach
分类号:
P315.69
DOI:
10.13232/j.cnki.jnju.2017.04.025
文献标志码:
A
摘要:
 由于符号属性数据缺乏固有的几何特性,不能简单地将现有的数值属性数据分类算法应用于符号属性数据.为了提高符号属性数据的性能,提出一种基于关联关系分析的支持向量机分类方法(Support Vector Machine Classification Approach Based on Correlation Analysis,CA_SVM).通过分析属性值与标签之间的相关性,得到属性值对标签的影响因子;然后结合属性值在类内出现的频率,使得所有原始符号数据下的属性值在不失信息的情况下转换成数值型数据;转换后的数据既可以体现属性值与标签之间的关联关系,也可以有效地表示相同属性下属性值之间的距离;最后用支持向量机(Support Vector Machine,SVM)进行分类.在标准UCI数据集上的实验结果表明,CA_SVM模型能够提高分类精度.
Abstract:
 Due to lack of geometric property between categorical data,the current classification algorithms for numerical data fail to deal with categorical data.To effectively improve the classifying performance in a set of categorical objects,we proposed a support vector machine classification approach based on correlation analysis,namely CA_SVM.By analyzing the correlation between attribute values and labels and the frequency of attributes in the class,we get the influence factors of attribute values on label.The approach,which can not only reflect the correlation between attribute values and labels,but also effectively expresses the distance between attribute values,may transform a set of categorical data into numerical data without losing information.The classifying performance of new proposed method was tested on data sets downloaded from the UCI.Results illustrate that the new proposed CA_SVM model increases the classifying accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
 基金项目:国家自然科学基金(61673249,61503229,61273291),山西省回国留学人员科研项目(2016-004),山西省自然科学青年基金(2015021096),山西省高等学校科技创新项目(2015110)
收稿日期:2017-06-08
*通讯联系人,E-mail:wjwang@sxu.edu.cn
更新日期/Last Update: 2017-08-03