Abstract:In order to solve the problem of reduction anomaly in the existing probabilistic rough set models, non-parameterized and parameterized maximum decision entropy measures for attribute reduction were proposed by using the concept of maximum confidence of uncertain object. The monotonicity of the parameterized maximum decision entropy was explained and the relationship between its attribute reduction and other ones was analyzed. The definitions for core and relatively dispensable attributes in the proposed model were also given. Moreover, non-parameterized and parameterized confidence discernibility matrixes were put forward and the difference of classical discernibility matrix and the proposed ones in charactering the uncertain object were discussed. Finally, a case study was given to show the validity of the proposed model.
PAWLAK Z. Rough sets [J]. International Journal of Computer and Information Science, 1982, 11(5):341-356.
[2]
PAWLAK Z. Rough sets: Theoretical aspects of reasoning about data [M]. Dordrecht: Kluwer Academic Publishers, 1991.
[3]
YAO Y Y. Probabilistic rough set approximations [J]. International Journal of Approximate Reasoning, 2008, 49(2):255-271.
[4]
ZIARKO W. Variable precision rough set model [J]. Journal of Computer and System Science, 1993, 46(1):39-59.
[5]
BEYNON M. Reducts within the variable precision rough sets model: An further investigation [J]. European Journal of Operational Research, 2001, 134(3):592-605.
[6]
WANG J Y, ZHOU J. Research of reduct features in the variable precision rough set model [J]. Neurocomputing, 2009, 72(10/11/12):2643-2648.
MI J S, WU W Z, ZHANG W X. Approaches to knowledge reduction based on variable precision rough set model [J]. Information Sciences, 2004, 159(3/4):255-272.