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.
Highlights
Uncertainty is associated with randomness, fuzziness, vagueness, roughness, and incomplete knowledge
We examine the granularity in the boundary regions from granular computing
The uncertainty analysis of five relative reductions in an inconsistent decision table is discussed for classification tasks from four aspects
Summary
Uncertainty is associated with randomness, fuzziness, vagueness, roughness, and incomplete knowledge. The theories of probability, information, fuzzy set, evidence set, rough set, and so forth have been used for uncertainty analysis [1]. The second one results from the approximation regions of rough sets, since the lower approximation is the certain region and the upper approximation is the possible region. This gives rise to a direction of analyzing uncertainty in relative reductions. The uncertainty analysis of five relative reductions is investigated in four aspects. In the remainder of this paper, some related notations are reviewed and uncertainty analysis of five relative reductions is discussed
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