Generalized Multigranulation Double-qu Antitative Decision-theoretic Rough Set And Attribute Reduction Research

With the rapid development of science and technology,massive data is presented in the field of science,economy and every aspect of social life.These data have the characteristics of huge amount of information,various types,low value density,fast processing speed and so on.How to get valuable information quickly and efficiently from the huge data collected from many sources is not only a hot spot in the research of information technology,but also a great opportunity and challenge in the field of artificial intelligence.In the increasingly complex data environment,more targeted data processing models need to be proposed in order to discover hidden knowledge and reveal the potential rules from the data.In the Pawlak rough set theory,the classification mechanism is established based on the indiscernibility relation,and the uncertainty information is described by the upper and lower approximation operators.Since 1982,it has been widely proved that it is an effective mathematical tool to express and deal with various kinds of incomplete information.With the deepening of the research,a large number of promotion models are proposed.Based on variable precision rough set and graded rough set,generalized multigranulation double-quantitative decision-theoretic rough set is first explored;secondly,logical disjunct operation of variable precision and graded rough set is proposed in ordered information system and intuitionistic fuzzy information;finally,attribute reduction of multi-source decision systems is explored in order to eliminate the influence of redundant information on the computational process and the final result.The main innovations are as follows:1.Based on the principle of the minority subordinate to the majority and the fault tolerance ability of double-quantitative decision-theoretic model,generalized multigranulation double-quantitative decision-theoretic rough set(GMDq RS)is explored.The upper and lower approximations of two kinds of GMDq RS are proposed based on the definition of upper and lower support characteristic functions.Then important properties,decision rules and internal relations of two models are studied.At the same time,the relationship between the two models and other models is explored.Finally,the classification advantage of two models is demonstrated through an illustrative case.The GMDq RS theory provides a theoretical basis for decision theory,multi-source information fusion and the generalization of generalized multigranulation theory.2.Rough set theory of ordered information system is given through logical disjunct double-quantitative indexes of variable precision and grade.Rough fuzzy set theory based on logical disjunct operation of variable precision and grade is proposed through the research on logical disjunct double-quantitative approximation characterization of a fuzzy concept.Then we define the dominance relation in the intuitionistic fuzzy system by the weighted score function of objects about attributes and propose rough set theory based on logical disjunct operation of variable precision and grade in the intuitionistic fuzzy system.At the same time,the basic structure and important properties of the two proposed models are studied.Finally,through a case further illustrates the proposed theory,and verify the rationality and validity of the model.3.The consistent attribute reduction is proposed based on the integrity of original valid information preservation in multi-source decision systems.At the same time,the attribute reduction based on conditional entropy fusion is proposed,which can be used to preserve partial original valid information and has strong applicability in practice.In addition,the definition of fuzzy similarity degree is given through the maximum-minimum closeness degree,Hamming closeness degree and Euclid closeness degree,and then the attribute reduction of multi-source fuzzy decision systems is explored.Finally,the attribute reduction theory of multi-source and multi-source fuzzy decision systems is expounded through a concrete case,which provides a theoretical basis for attribute reduction of rough set models.