A rough set based GDSS approach to integrate multi-type preference information

Tian Fei, Liu Lu, You Weijia
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Abstract

Two problems in multi-attribute group decision process are studied: integration of multi-types of preference information and making use of given decisions. And a GDSS model based on rough set is proposed, which includes seven common ways for representing preference information. An improved Sem Naive Scaler Algorithm is also suggested to decentralize the information in the rough set table. Finally, rules based on dominance-based rough set theory are applied to the information table to rank all the alternatives.
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基于粗糙集的gds多类型偏好信息集成方法
研究了多属性群体决策过程中的两个问题:多类型偏好信息的整合和给定决策的利用。提出了一种基于粗糙集的gds模型,该模型包含7种常用的偏好信息表示方法。提出了一种改进的Sem朴素标量算法来分散粗糙集表中的信息。最后,将基于优势粗糙集理论的规则应用到信息表中,对所有备选方案进行排序。
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