{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T05:26:13Z","timestamp":1741065973474,"version":"3.38.0"},"reference-count":17,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2012,6,7]],"date-time":"2012-06-07T00:00:00Z","timestamp":1339027200000},"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":["Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering"],"published-print":{"date-parts":[[2012,8]]},"abstract":"<jats:p> Rule extraction is viewed as an important pretreatment step for machine learning and data mining. In allusion to the shortcomings of the rough set method for rule extraction in systems with incomplete fault diagnosis, in this paper we propose a novel fusion approach based on rough set theory and particle swarm optimization for rule extraction (RSTPSORE) in order to improve the diagnostic robustness and accuracy. In the proposed method, first, an iterative linear subsection interpolation completion method is used to achieve completion of the fault diagnosis process. Then rough set theory is used to find a subset which can preserve the meaning of the attributes. Particle swarm optimization is used to discover the best rule within the flying subset space. To verify the efficiency of the proposed method, experiments are carried out on the collected data in a centralized control substation incomplete fault diagnosis system. The results indicate that the proposed method can provide an efficient solution to finding a minimal subset. <\/jats:p>","DOI":"10.1177\/0959651812445010","type":"journal-article","created":{"date-parts":[[2012,6,8]],"date-time":"2012-06-08T03:55:32Z","timestamp":1339127732000},"page":"904-913","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["An efficient fusion approach to rule extraction based on rough set theory and particle swarm optimization and its application"],"prefix":"10.1177","volume":"226","author":[{"given":"Wu","family":"Deng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, People\u2019s Republic of China"},{"name":"Software Institute, Dalian Jiaotong University, People\u2019s Republic of China"},{"name":"Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, People\u2019s Republic of China"},{"name":"Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, People\u2019s Republic of China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, People\u2019s Republic of China"}]},{"given":"Xinhua","family":"Yang","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, People\u2019s Republic of China"}]},{"given":"Li","family":"Zou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, People\u2019s Republic of China"},{"name":"Software Institute, Dalian Jiaotong University, People\u2019s Republic of China"},{"name":"Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, People\u2019s Republic of China"}]},{"given":"Huimin","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, People\u2019s Republic of China"},{"name":"Software Institute, Dalian Jiaotong University, People\u2019s Republic of China"},{"name":"Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, People\u2019s Republic of China"},{"name":"Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, People\u2019s Republic of China"}]},{"given":"Wen","family":"Li","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, People\u2019s Republic of 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