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Rough sets and neural networks are the most common techniques applied in data mining problems. In order to extract useful knowledge and classify ambiguous patterns effectively, this paper presented a hybrid algorithm based on the integration of rough sets and BP neural network to construct a novel classification system. The attribution values were discretized through PSO algorithm firstly to establish a decision table. The attribution reduction algorithm and rules extraction method based on rough sets were proposed, and the flowchart of proposed approach was designed. Finally, a prototype system was developed and some simulation examples were carried out. Simulation results indicated that the proposed approach was feasible and accurate and was outperforming others.<\/jats:p>","DOI":"10.1155\/2014\/797432","type":"journal-article","created":{"date-parts":[[2014,4,24]],"date-time":"2014-04-24T17:13:45Z","timestamp":1398359625000},"page":"1-11","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Classification Approach through Integration of Rough Sets and Back-Propagation Neural Network"],"prefix":"10.1155","volume":"2014","author":[{"given":"Lei","family":"Si","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin-hua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China"},{"name":"Xuyi Mine Equipment and Materials R&D Center, China University of Mining & Technology, 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