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Based on the insights obtained, a multi-measure feature selection algorithm is developed for ordered data, which not only considers the certain information by the dominance-based dependence, but also uses the discern information provided by the dominance-based information granularity. Extensive experiments are performed to evaluate the performance of the proposed algorithm on UCI data sets in terms of the number of selected feature subset and classification accuracy. The experimental results demonstrate that the proposed algorithm not only can find the relevant feature subset but also the classification performance is better than, or comparably well to other feature selection algorithms.<\/jats:p>","DOI":"10.3233\/jifs-224474","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T11:39:13Z","timestamp":1681817953000},"page":"3379-3392","source":"Crossref","is-referenced-by-count":0,"title":["A multi-measure feature selection method for decision systems with preference relation"],"prefix":"10.1177","volume":"45","author":[{"given":"Wenhao","family":"Shu","sequence":"first","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang, China"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang, 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