Calculation Analysis and Attribute Reduction for Double-Quantitative Rough Set Model Based on Logical OR
ZHANG Xian-Yong1,2,3, MIAO Duo-Qian2,3
1.College of Mathematics and Software Science, Sichuan Normal University, Chengdu 610068 2.Department of Computer Science and Technology, Tongji University, Shanghai 201804 3.Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804
Abstract：Double quantification has a fundamental function for completely describing approximate space in rough set, and the rough set model based on logical OR of precision and grade is a basic extended model with double quantification. Aiming at this model, calculation analysis is mainly conducted, and attribute reduction in approximate space is further explored. Based on both basic structures and calculation formulas of model regions, macroscopic algorithm and structural algorithm are constructed. The analysis and comparison results show that the structural algorithm has more advantages in calculation complexity. Based on approximate space, basic properties on four-region preservation are discussed, and attribute reduction with the region preservation criterion is proposed and investigated. In particular, a type of extended quantitative reduction is obtained for the classical qualitative reduction. Some generalized thoughts are provided for optimal calculations and reduction applications of double-quantitative rough set models.
张贤勇，苗夺谦. 基于逻辑或的双量化粗糙集模型的计算分析与属性约简*[J]. 模式识别与人工智能, 2014, 27(9): 778-786.
ZHANG Xian-Yong, MIAO Duo-Qian. Calculation Analysis and Attribute Reduction for Double-Quantitative Rough Set Model Based on Logical OR. , 2014, 27(9): 778-786.
[1] Pawlak Z. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356 [2] Ziarko W. Variable Precision Rough Set Model. Journal of Computer and System Sciences, 1993, 46(1): 39-59 [3] Yao Y Y, Lin T Y. Generalization of Rough Sets Using Modal Logics. Intelligent Automation and Soft Computing, 1996, 2(2): 103-120 [4] Inuiguchi M, Yoshioka Y, Kusunoki Y. Variable-Precision Dominance-Based Rough Set Approach and Attribute Reduction. International Journal of Approximate Reasoning, 2009, 50(8): 1199-1214 [5] Wang J Y, Zhou J. Research of Reduct Features in the Variable Precision Rough Set Model. Neurocomputing, 2009, 72(10/11/12): 2643-2648 [6] Beynon M. Reducts within the Variable Precision Rough Sets Model: A Further Investigation. European Journal of Operational Research, 2001, 134(3): 592-605 [7] Mi J S, Wu W Z, Zhang W X. Approaches to Knowledge Reduction Based on Variable Precision Rough Set Model. Information Sciences, 2004, 159(3/4): 255-272 [8] Yanto I T R, Vitasari P, Herawan T, et al. Applying Variable Precision Rough Set Model for Clustering Student Suffering Study′s Anxiety. Expert Systems with Applications, 2012, 39(1): 452-459 [9] Xie F, Lin Y, Ren W W. Optimizing Model for Land Use/Land Cover Retrieval from Remote Sensing Imagery Based on Variable Precision Rough Sets. Ecological Modeling, 2011, 222(2): 232-240 [10] Yao Y Y, Lin T Y. Graded Rough Set Approximations Based on Nested Neighborhood Systems // Proc of the 5th European Congress on Intelligent Techniques and Soft Computing. Aachen, Germany, 1997: 196-200 [11] Liu C H, Miao D Q, Zhang N. Graded Rough Set Model Based on Two Universes and Its Properties. Knowledge-Based Systems, 2012, 33: 65-72 [12] Zhang X Y, Mo Z W, Xiong F, et al. Comparative Study of Variable Precision Rough Set Model and Graded Rough Set Model. International Journal of Approximate Reasoning, 2012, 53(1): 104-116 [13] Zhang X Y, Miao D Q. Two Basic Double-Quantitative Rough Set Models of Precision and Grade and Their Investigation Using Granular Computing. International Journal of Approximate Reasoning, 2013, 54(8): 1130-1148 [14] Zhang X Y, Xiong F, Mo Z W. Rough Set Model Based on Logical OR Operation of Precision and Grade. Pattern Recognition and Artificial Intelligence, 2009, 22(5): 697-703 (in Chinese) (张贤勇,熊 方,莫智文.精度与程度的逻辑或粗糙集模型.模式识别与人工智能, 2009, 22(5): 697-703) [15] Zhang X Y, Miao D Q. Quantitative Information Architecture, Granular Computing and Rough Set Models in the Double-Quantitative Approximation Space of Precision and Grade. Information Sciences, 2014, 268: 147-168
2014, Vol. 27 
