摘要
应用证据理论的一个关键问题是生成基本概率指派(BPA),目前如何生成BPA仍然是一个有待解决的问题.本文提出一种基于区间数的BPA生成方法,首先建立样本属性的区间数模型,然后用区间数的距离表示样本属性之间的差异性,在此基础上提出了一种相似度,最后对相似度进行归一化得到BPA.通过鸢尾花数据集(Iris DataSet)的分类实验验证了该方法的有效性,结论表明整体识别率为96%.本文方法简单实用,数据样本较少情况下也能有效确定BPA.
One of the open issues of Dempster Shafer theory is how to determine basic probability assignment function （BPA）. To solve this problem, a method to determine BPA based on interval numbers is proposed in the paper. At first, the model of interval numbers is conslructed with the samples. Then the distance of interval numbers is used to represent difference among the attributes of the samples,so the similarity of them is calculaled. At last,the similarity is normalized to get the value of BPA. The ef fectiveness of this method is proved by classifying the Iris Set. It concludes that the total recognition rate is 96 %. This method is simple and practical;it can determine BPA in the case of the little number of the samples.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2012年第6期1092-1096,共5页
Acta Electronica Sinica
基金
国家自然科学基金(No.60874105
No.61174022)
教育部新世纪优秀人才支持计划(No.NCET-08-0345)
重庆市自然科学基金(No.CSCT
2010BA2003)
关键词
证据理论
BPA
区间数
相似度
数据融合
分类
evidence theory
BPA
interval number
similarity
data fusion
recognition
