%A Jie Ma,Yan Ge,Hongyu Pu %T Survey of Attribute Reduction Methods %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.1278 %P 40-50 %V 4 %N 1 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4760.shtml} %8 2020-01-25 %X

[Objective] This paper reviews the methods, developing trends and applications of attribute reduction, aiming to support systematic research in this field.[Coverage] From the Web of Science and CNKI, we retrieved 142 articles on attribute reduction, using the keywords of “Attribute Reduction” and “属性约简”. We also optimized the results with topic selection, intensive reading and retrospective method.[Methods] We surveyed the fundamentals of attribute reduction, and then summarized its leading research.[Results] The popular research of attribute reduction methods focused on rough sets, granular computing and formal concept analysis. Its developing trends were closely related to the dynamics of data and the fusion of intelligent algorithms.[Limitations] We only briefly discussed the merging of attribute reduction algorithms.[Conclusions] We explored the developing trends of attribute reduction methods.