A Multi-Level Rough Set Model Based on Attribute Value Taxonomies
YE Ming-Quan1,3,HU Xue-Gang1,HU Dong-Hui1,WU Xin-Dong1,2
1.School of Computer and Information,Hefei University of Technology,Hefei 230009 2.Department of Computer Science,University of Vermont,Burlington,VT 05405,USA 3.Department of Computer Science,Wannan Medical College,Wuhu 241002
Abstract Most traditional studies on rough sets focus on finding attribute reduction and decision rules on the single level decision tables. However,attribute value taxonomies (AVTs) are usually predefined in applications and represented by hierarchy trees. Aiming at the attribute value taxonomies for condition attributes,the classical rough set model is extended to a multi-level rough set (MLRS) model combining with the full-subtree generalization mode. With decision table at different levels of generalization space,some properties of MLRS are obtained. Paralleling with attribute reduction based on positive region,a concept of attribute value generalization reduction in MLRS is introduced and the relations of generalization reduction and attribute reduction are analyzed. The computation of the generalization reduction in MLRS is proved to be a NP-hard problem. Then,a heuristic algorithm of generalization reduction based on the positive region of MLRS is proposed,which utilizes attribute value taxonomies to make top-down refinements. The attribute values are generalized to the optimal levels of their AVTs by the proposed algorithm,while the original positive region of the decision table keeps invariant. Theoretical analysis and simulation experiments illustrate that generalization reduction method improves the level and the generalization ability of knowledge discovery.
2013, Vol. 26 
