Research On Covering Rough Sets Model Reduction Based On Pythagorean Fuzzy Environment

Abundant information resources have brought us valuable wealth.In data mining,there are many problems that traditional mathematical tools are difficult to solve,including incomplete information,uncertain information and so on.Therefore,many uncertainty theories have come into being.Fuzzy set,rough set and their generalized theories are always the research hotspots in this field.As a generalization of fuzzy set,Pythagorean fuzzy set has good applicability and the scope of processing data is more extensive.The research on Pythagorean fuzzy set is fruitful,but the cross research among Pythagorean fuzzy set,fuzzy set and their extension theories is relatively weak.In view of this,we investigate the attribute reduction of Pythagorean fuzzy rough set and Pythagorean fuzzy-covering rough set in Pythagorean fuzzy environment.The main research contents are as follows:1.Pythagorean fuzzy rough sets are constructed by using Pythagorean fuzzy equivalence relation.With the help of information entropy,the attribute reduction method of the model is established,the relationship between kernel and reduction is discussed,and the corresponding reduction algorithm is designed.Finally,an example is provided to verify the feasibility of the reduction method.2.By using covering rough set,Pythagorean fuzzy -covering rough set model is established.We also define the Pythagorean fuzzy -covering neighborhood system and compare the new model with other existing models to illustrate the advantages of this method.3.Based on Pythagorean fuzzy -covering rough set,its decision system is divided into the consistent decision system and inconsistent decision system.The discernibility matrix is defined by the relationship between element neighborhoods.By using the discernibility function,attribute reduction under two systems is investigated,and the related properties are discussed.Finally,we further design the corresponding reduction algorithms and illustrate their the effectiveness and practicability through experimental analysis.