文章基本信息

标题：An improved method to determine basic probability assignment with interval number and its application in classification
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作者：Bowen Qin ; Fuyuan Xiao
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：1
页码：1
DOI：10.1177/1550147718820524
出版社：Hindawi Publishing Corporation
摘要：Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k -means cluster method is proposed to determine basic probability assignment. At first, k -means clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on distance and similarity of interval number between the test sample and constructed models are defined to generate basic probability assignment. Finally, Dempster’s combination rule is used to combine multiple basic probability assignments to get the final basic probability assignment. The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.
关键词：Dempster–Shafer evidence theory; basic probability assignment; interval number; recognition; k-means