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Rough set theory is an efficient information processing tool used in the discovery of data dependencies. It evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. This paper presents a method for attribute reduction on combination of rough set and neighborhood systems. Neighborhood decision system...
Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. Rough set theory offers a viable approach for feature selection from data sets. In this paper, the basic concepts of rough set theory and ant colony optimization are introduced, and the role of the basic constructs of rough set approach in feature selection, namely attribute reduction is...
Reduct finding, especially optimal reduct finding, similar to feature selection problem, is a crucial task in rough set applications to data mining. In this paper, we have studied the basic concepts of rough set theory, and discussed several special cases of the ant colony optimization metaheuristic algorithms. Based on the above study, we propose a feature selection algorithm within a mixed framework...
Notice of Violation of IEEE Publication Principles"Cognitive Overhead Reducing Based on Collaborative Filtering in Project Management,"by Hua Chen, Qin-Ming He, Jian-Fei Qianin the Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, August 2006After careful and considered review of the content and authorship of this paper by a duly constituted expert committee,...
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