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Attribute reduction in decision-theoretic rough set model using mapreduce

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014) 8818 601-612
DOI: 10.1007/978-3-319-11740-9_55
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Abstract

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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Qian, J., Lv, P., Guo, Q., & Yue, X. (2014). Attribute reduction in decision-theoretic rough set model using mapreduce. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8818, pp. 601–612). Springer Verlag. https://doi.org/10.1007/978-3-319-11740-9_55

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