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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\u2019s 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%.<\/jats:p>","DOI":"10.1177\/1550147718820524","type":"journal-article","created":{"date-parts":[[2019,1,4]],"date-time":"2019-01-04T06:37:10Z","timestamp":1546583830000},"page":"155014771882052","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["An improved method to determine basic probability assignment with interval number and its application in classification"],"prefix":"10.1177","volume":"15","author":[{"given":"Bowen","family":"Qin","sequence":"first","affiliation":[{"name":"School of Computer and Information Science, Southwest University, Chongqing, 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