{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:17:32Z","timestamp":1772122652000,"version":"3.50.1"},"reference-count":0,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p>Based on analysis of Pawlak's rough set model in the view of single equivalence relation and the theory of fuzzy set, associated with multi-granulation rough set models proposed by Qian, two types of new rough set models are constructed, which are multi-granulation fuzzy rough sets. It follows the research on the properties of the lower and upper approximations of the new multi-granulation fuzzy rough set models. Then it can be found that the Pawlak rough set model, fuzzy rough set model and multi-granulation rough set models are special cases of the new one from the perspective of the considered concepts and granular computing. The notion of rough measure and (\u03b1, \u03b2)-rough measure which are used to measure uncertainty in multi-granulation fuzzy rough sets are introduced and some basic properties of the measures are examined. The construction of the multi-granulation fuzzy rough set model is a meaningful contribution in the view of the generalization of the classical rough set model.<\/jats:p>","DOI":"10.3233\/ifs-130818","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T18:20:50Z","timestamp":1575310850000},"page":"1323-1340","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":58,"title":["Multi-granulation fuzzy rough sets"],"prefix":"10.1177","volume":"26","author":[{"given":"Weihua","family":"Xu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Chongqing University of Technology, Chongqing, P.R. China"},{"name":"School of Management, Xi'an Jiaotong University, Xi'an, P.R. China"}]},{"given":"Qiaorong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Chongqing University of Technology, Chongqing, P.R. China"}]},{"given":"Shuqun","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Chongqing University of Technology, Chongqing, P.R. China"}]}],"member":"179","published-online":{"date-parts":[[2014,1]]},"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-130818","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-130818","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:57:07Z","timestamp":1770814627000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IFS-130818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,1]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2014,3]]}},"alternative-id":["10.3233\/IFS-130818"],"URL":"https:\/\/doi.org\/10.3233\/ifs-130818","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,1]]}}}