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Often these problems can be modeled by Dempster\u2013Shafer theory. In Dempster\u2013Shafer theory, the most primary processing unit is the basic probability assignment, which is a description of objective information in the real world. How to make this description more effective is a vital but open issue. A novel basic probability assignment generation model is proposed in this article whose objective is to provide perspective with respect to how basic probability assignment can be determined based on learning algorithms. First, the basic probability assignment generation model is constructed based on clustering idea using K-means method, which is employed to determine basic probability assignment with the proposed basic probability assignment generation method. Moreover, the proposed basic probability assignment generation method is extended by K\u2013nearest neighbor (K-NN) algorithm. The detailed implementation of the proposed method is demonstrated by several numerical examples. As an extension, a classifier called KKC is constructed according to the developed approach, and its classification effect is compared with several famous classification algorithms. Experiments manifest desirable results with regard to classification accuracy, which illustrates the applicability of the proposed method to determine basic probability assignment. <\/jats:p>","DOI":"10.1177\/1550147719865876","type":"journal-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T05:20:05Z","timestamp":1564464005000},"page":"155014771986587","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":30,"title":["A novel method to determine basic probability assignment in Dempster\u2013Shafer theory and its application in multi-sensor information fusion"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5417-6130","authenticated-orcid":false,"given":"Liguo","family":"Fei","sequence":"first","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqiang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luning","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,7,30]]},"reference":[{"key":"bibr1-1550147719865876","volume-title":"Mathematical techniques in multisensor data fusion","author":"Hall DL","year":"2004"},{"key":"bibr2-1550147719865876","doi-asserted-by":"publisher","DOI":"10.1109\/5.554205"},{"key":"bibr3-1550147719865876","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177698950"},{"key":"bibr4-1550147719865876","unstructured":"Shafer G. 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