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However, SMOTE creates synthetic objects in a random way, therefore it produces a different result each time it is applied, and in practice the user has to apply SMOTE several times for choosing the best of all the generated balanced datasets. For this reason, in this paper, we present SMOTE-D, a deterministic version of SMOTE, and propose new deterministic SMOTE-D-based versions of some of the most recent and successful SMOTE-based methods. In our experiments, we show that all proposed deterministic methods produce as good results as random methods but our proposals need to be applied just once. This is very important from a practical point of view since our proposals save time by avoiding multiple applications of them as SMOTE does and they provide one unique result.<\/jats:p>","DOI":"10.3233\/jifs-179041","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T12:10:26Z","timestamp":1557835826000},"page":"4945-4955","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Deterministic oversampling methods based on SMOTE"],"prefix":"10.1177","volume":"36","author":[{"given":"Fredy","family":"Rodriguez-Torres","sequence":"first","affiliation":[{"name":"Computer Science Department, Instituto Nacional de Astrof\u00edsica \u00d3ptica y Electr\u00f3nica, Luis Enrique Erro No.1, Tonatzintla, Puebla, Pue, Mexico"}]},{"given":"Jes\u00fas A.","family":"Carrasco-Ochoa","sequence":"additional","affiliation":[{"name":"Computer Science Department, Instituto Nacional de 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