A Dominance Degree for Rough Sets and Its Application in Ranking Popularity - Jia Zhao; Jianfeng Guan; Changqiao Xu; Hongke Zhang (Institute of Advanced Engineering and Science, 2014)
Rough set theory is used in data mining through complex learning systems and uncertain information decision from artificial intelligence. For multiple attribute decision making, rough sets employ attribute reduction to generate decisive rules. However, dynamic information databases, which record attribute values changing with time, raise questions to rough set based multiple attribute reduction. This paper proposes a dynamic attribute based dominance degree for rough-set-based ranking decision. According to the dominance relations between two objects in dynamic information table, we propose three judgments and their judging values to get a dominance degree value, by which we can deny or approve of the dominance relations. Then we use the dominance-degree-based rough set to make dynamic attribute reduction. We applied this method in ranking popularity of network service resources. and extract ranking decision rules. Experiments show comparison between the searching engines with and without the ranking function and the efficiency of rough set ranking by our proposed dominance degree value.