Rough set model under a limited asymmetric similarity relation and an approach for incremental updating approximations

Cui, Mingyuan

doi:10.1016/j.phpro.2012.02.089

Cited by 1 publication

(1 citation statement)

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“…Kryszkiewicz () proposed a method to derive decision rules from an information table based on the similarity relation. Cui () presented a rough set model under a limited asymmetric similarity relation. Abo‐Tabl () investigated two kinds of rough approximations under the similarity relation.…”

Section: Introductionmentioning

confidence: 99%

A Rough Programming Model Based on the Greatest Compatible Classes and Synthesis Effect

Jin

Jing

et al. 2013

Syst. Res.

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The globalization connects different parts of the world tightly, one region can be closely interacted by another region. The globalized environment can become dynamic and turbulent, thus brings uncertainties into decision making. A critical challenge in system science is to deal with the uncertainties such as fuzziness, randomness and roughness of information. In this paper, a programming model in rough sets is presented. First, the characteristics and limitations of the existing rough programming methods are analysed systematically. Second, the necessity and feasibility of developing a new rough programming model is discussed, and the model is developed on the basis of the greatest compatible classes and synthesis effect. Finally, the effectiveness and characteristics of the newly developed model are validated through a case study. The result illustrates that the new programming model is of significance in practical applications, and it makes it possible to take decision preferences into account of the decision-making processes effectively.

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Section: Introductionmentioning

confidence: 99%