{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:53:43Z","timestamp":1781196823215,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In the framework of evidence theory, one of the open and crucial issues is how to determine the basic probability assignment (BPA), which is directly related to whether the decision result is correct. This paper proposes a novel method for obtaining BPA based on Adaboost. The method uses training data to generate multiple strong classifiers for each attribute model, which is used to determine the BPA of the singleton proposition since the weights of classification provide necessary information for fundamental hypotheses. The BPA of the composite proposition is quantified by calculating the area ratio of the singleton proposition\u2019s intersection region. The recursive formula of the area ratio of the intersection region is proposed, which is very useful for computer calculation. Finally, BPAs are combined by Dempster\u2019s rule of combination. Using the proposed method to classify the Iris dataset, the experiment concludes that the total recognition rate is 96.53% and the classification accuracy is 90% when the training percentage is 10%. For the other datasets, the experiment results also show that the proposed method is reasonable and effective, and the proposed method performs well in the case of insufficient samples.<\/jats:p>","DOI":"10.3390\/e23070812","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T11:07:40Z","timestamp":1624619260000},"page":"812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5476-6448","authenticated-orcid":false,"given":"Wei","family":"Fu","sequence":"first","affiliation":[{"name":"Department of Automation, Heilongjiang University, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Automation, Heilongjiang University, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7955-2884","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Automation, Heilongjiang University, Harbin 150080, China"},{"name":"Key Laboratory of Information Fusion Estimation and Detection in Heilongjiang Province, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.promfg.2020.02.255","article-title":"Methods and analytical tools for assessing tactical situation in military operations using potential approach and sensor data fusion","volume":"44","author":"Chmielewski","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.comcom.2019.12.039","article-title":"Unmanned aerial vehicle\u2019s runway landing system with efficient target detection by using morphological fusion for military surveillance system","volume":"151","author":"Nagarani","year":"2020","journal-title":"Comput. 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