{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:48:43Z","timestamp":1777704523476,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,4,22]]},"abstract":"<jats:p>Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, \u03b8-reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction algorithms base on \u03b8-discernibility matrix, \u03b8-information entropy and \u03b8-significance in an IIVIS are given.<\/jats:p>","DOI":"10.3233\/jifs-200394","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T13:04:58Z","timestamp":1614690298000},"page":"8775-8792","source":"Crossref","is-referenced-by-count":2,"title":["Attribute selection approaches for incomplete interval-value data"],"prefix":"10.1177","volume":"40","author":[{"given":"Zhaowen","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Complex System Optimization and Big Data Processing in Department of GuangxiEducation, Yulin Normal University, Yulin, Guangxi, P.R. 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