title: Uncertainty analysis of knowledge reductions in rough sets.
abstract: Uncertainty analysis is a vital issue in intelligent information processing, especially in the age of big data. Rough set theory has attracted much attention to this field since it was proposed. Relative reduction is an important problem of rough set theory. Different relative reductions have been investigated for preserving some specific classification abilities in various applications. This paper examines the uncertainty analysis of five different relative reductions in four aspects, that is, reducts' relationship, boundary region granularity, rules variance, and uncertainty measure according to a constructed decision table.
authors.affiliation: Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai 201804, China.
authors.forename: Ying
authors.identifier: 0000-0001-5451-7660
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Computer and Control Engineering, Yantai University, 32 Qingquan Road, Shandong 264005, China.
authors.forename: Nan
authors.initials: N
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D003658:Decision Making, Computer-Assisted; D008962:Models, Theoretical; D015203:Reproducibility of Results; D035501:Uncertainty