[1]刘邱云,王璐璐,黄 涛.一种基于Logistic回归的基本信度分配函数的构造新方法[J].江西师范大学学报(自然科学版),2022,(03):277-281.[doi:10.16357/j.cnki.issn1000-5862.2022.03.10]
　LIU Qiuyun,WANG Lulu,HUANG Tao.The New Method Based on Logistic Regression of Constructing Basic Belief Assignment Function[J].Journal of Jiangxi Normal University:Natural Science Edition,2022,(03):277-281.[doi:10.16357/j.cnki.issn1000-5862.2022.03.10]
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一种基于Logistic回归的基本信度分配函数的构造新方法()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2022年03期
页码:
277-281
栏目:
数学与应用数学
出版日期:
2022-05-25

文章信息/Info

Title:
The New Method Based on Logistic Regression of Constructing Basic Belief Assignment Function
文章编号:
1000-5862(2022)03-0277-05
作者:
刘邱云1; 王璐璐2; 黄 涛1
1.江西师范大学数学与统计学院,江西 南昌 330022; 2.江西经济管理干部学院财务与金融学院,江西 南昌 330088
Author(s):
LIU Qiuyun1; WANG Lulu2; HUANG Tao1
1.School of Mathematics and Statistics,Jiangxi Normal University,Nanchang Jiangxi 330022,China; 2.School of Accounting and Finance,Jiangxi Institute of Economic Administrators,Nanchang Jiangxi 330088,China
关键词:
Logistic回归;  基本信度分配函数;  加权D-S证据融合;  图像分类
Keywords:
Logistic regression;  basic belief assignment function;  weighted D-S evidence fusion;  image classification
分类号:
TP 18
DOI:
10.16357/j.cnki.issn1000-5862.2022.03.10
文献标志码:
A
摘要:
结合Logistic回归分类,该文提出一种新的构造证据理论基本信度分配函数的方法,并将其应用于多特征图像分类.该方法首先以多类Logistic回归分类法输出的后验概率与样本分类正确率建立证据权重系数,然后构造出加权的基本信度分配函数,最后利用加权D-S证据融合判别所属类别.实验结果显示:该方法既能提高图像分类的正确率,又能改正使用单特征分类导致的分类正确率的不稳定的缺点.
Abstract:
Combined with Logistic regression classification,the new method of constructing the basic belief assignment function of evidence theory is presented,and it is applied in the multi-feature image classification.Firstly,the weight coefficient of the evidence is established based on the posterior probability output by multi-class Logistic regression classification method and the samples' classification accuracy.Secondly,the weighted basic belief assignment function is constructed.Finally,the weighted D-S evidence fusion is used to distinguish the category.Experimental results show that the new method can not only improve the accuracy of image classification,but also overcome the instability of classification accuracy caused by single feature classification.

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备注/Memo

备注/Memo:
收稿日期:2021-11-04
基金项目:江西省教育厅科学技术研究(GJJ181392, GJJ191687)资助项目.
作者简介:刘邱云(1976-),女,江西铜鼓人,讲师,主要从事不确定性推理和描述逻辑的研究.Email:lqyxinxiang@126.com
更新日期/Last Update: 2022-05-25