一种基于Logistic回归的基本信度分配函数的构造新方法  

The New Method Based on Logistic Regression of Constructing Basic Belief Assignment Function

作　　者：刘邱云[1] 王璐璐[2] 黄涛[1] LIU Qiuyun;WANG Lulu;HUANG Tao(School of Mathematics and Statistics,Jiangxi Normal University,Nanchang Jiangxi 330022,China;School of Accounting and Finance,Jiangxi Institute of Economic Administrators,Nanchang Jiangxi 330088,China)

机构地区：[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

出　　处：《江西师范大学学报（自然科学版）》2022年第3期277-281,共5页Journal of Jiangxi Normal University(Natural Science Edition)

基　　金：江西省教育厅科学技术研究(GJJ181392,GJJ191687)资助项目.

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.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.

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]