Attribute Reduction in Decision-Theoretic Rough Set Model Using MapReduce

Attribute reduction is one of the most important research issues in decision-theoretic rough set model. This paper studies a new attribute measure preserving boundary region partition for a reduct. The relationships among the positive region, the probabilistic positive region and the indiscernibility object pairs for an equivalence class are analyzed. A heuristic attribute reduction algorithm framework using MapReduce in decision-theoretic rough set model is proposed. This study gives some insights into how to conduct attribute reduction in decision-theoretic rough set for big data.

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