{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:26:59Z","timestamp":1762522019511,"version":"3.37.3"},"reference-count":60,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2017,4,1]],"date-time":"2017-04-01T00:00:00Z","timestamp":1491004800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61602052"],"award-info":[{"award-number":["61602052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2017,4]]},"abstract":"<jats:p> In previous work, imbalanced datasets composed of more benign samples (the majority class) than the malicious one (the minority class) have been widely adopted in Android malware detection. These imbalanced datasets bias learning toward the majority class, so that the minority class examples are more likely to be misclassified. To solve the problem, we propose a new oversampling method called fuzzy\u2013synthetic minority oversampling technique, which is based on fuzzy set theory and the synthetic minority oversampling technique method. As the sample size of the majority class increases relative to that of the minority class, fuzzy\u2013synthetic minority oversampling technique generates more synthetic examples for each minority class examples in the fuzzy region, where the minority examples have a low degree of membership to the minority class and are more likely to be misclassified. Using the new synthetic examples, the classifiers build larger decision regions that contain more minority examples, and they are no longer biased to the majority class. Compared with synthetic minority oversampling technique and Borderline\u2013synthetic minority oversampling technique methods, fuzzy\u2013synthetic minority oversampling technique achieves higher accuracy on both the minority class and the entire datasets. <\/jats:p>","DOI":"10.1177\/1550147717703116","type":"journal-article","created":{"date-parts":[[2017,4,8]],"date-time":"2017-04-08T10:58:03Z","timestamp":1491649083000},"page":"155014771770311","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Fuzzy\u2013synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets"],"prefix":"10.1177","volume":"13","author":[{"given":"Yanping","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 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