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Search Results: 1 - 10 of 301352 matches for " approximate operators<br>粗糙集 "
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Generalizing rough set theory based on constructive method

YU Hong,

重庆邮电大学学报(自然科学版) , 2009,
Abstract: The generalization of the rough set theory was reviewed from the angle of constructive method. First the basic ideas and framework of the rough set theory and the different views of knowledge representation in rough set theory were introduced, and then the upper and lower approximate operators definitions were discussed respectively in view of element based, granular based, subsystem based, and probability. Furthermore, the recent studies for the generalization of the theory and the future development trend of the rough set theory were studied.
The Extension of the Rough Set Model

王梦洁, 许爱琴, 刘永坚, 王卫华
Computer Science and Application (CSA) , 2013, DOI: 10.12677/CSA.2013.34039
Because of the incomplete information and inequivalent binary relation on the domain, the classic rough set model will need to be expanded. On the one hand, the equivalence relations of the rough set model is extended to tolerance relations or inclusion relations, which can extend the application range. This paper studies high and low approximation operators on the basis of this method and compares with the two kinds of relations through case analysis. On the other hand, on the basic of granularity of knowledge structure and knowledge representation method, it studies the approximation operators from two aspects of neighborhood system and granularity.
Note of Covering Rough Set Model Nature

LIANG Jun-qi,YAN Shu-xia,

计算机科学 , 2011,
Abstract: We promoted part nature of covering rough set model, introduced a pair of new operator and extended the in elusion relation of join and intersection of the upper and lower approximation sets, then got some better results.
On the Generalization of Variable Precision Covering Rough Set Model

SUN Shi-bao,QIN Ke-yun,

计算机科学 , 2008,
Abstract: 基于多数包含关系及误差参数β(0≤β<O.5),提出了两种基于对象邻域的变精度覆盖粗糙集模型,讨论了模型中β上、下近似算子的性质;从对偶性角度出发推广了β上近似、β下近似算子apcβ(X)与^—apcβ(X),得到了两对对偶的上、下近似算子apc′β(X)与^—apc′β(X)和apc″β(X)与^—apc″β(X);研究了这些近似算子的性质及相互关系。
On Compounds of Binary Relations and Compositions of Approximation Operators

XU You-hong,

计算机科学 , 2009,
Abstract: Concepts of various types of crisp binary relations as well as fuzzy binary relations were defined,compounds of binary relations were investigated and their properties were examined.Compositions of approximation spaces and app-roximation operators were also defined.The relationship between approximation operators derived from two approximation spaces and the ones from the compound approximation spaces was investigated.It was proved that approximation operators induced from a compound of two approximation sp...
Rough Set Based on Logical AND and OR of Equivalence Relations

徐伟华, 张先韬, 王巧荣
Hans Journal of Data Mining (HJDM) , 2011, DOI: 10.12677/hjdm.2011.11002
Abstract: 本文从关系逻辑运算的角度研究粗糙集,对经典的粗糙集进行了推广。对多个等价关系进行逻辑“与”和逻辑“或”运算,提出了逻辑“与”粗糙集模型和逻辑“或”粗糙集模型。说明了逻辑“与”粗糙集模型和Pawlak经典粗糙集的关系,并详细研究了逻辑“或”粗糙集模型的重要性质,定义了逻辑“或”粗糙集模型中的若干度量,举例验证了该模型。
We popularize the classical rough set model in this paper and study rough set in the view of logical operation of equivalence relations. The logical AND rough set model and the logical OR rough set model are proposed on the basis of the operations logical AND and logical OR of equivalence relations. Furthermore, the connection between the logical AND rough set model and Pawlak’s classical rough set model is illustrated. Important properties are discussed in depth and several measures are defined in OR-RS. An example is em-ployed to explain OR-RS.
Approximate Decision Rules and Matching Rules in Rough Set Based Classification Algorithms

ZHANG Xue-ying,LIU Feng-yu,Jürgen Krause,

计算机科学 , 2005,
Abstract: In most cases,the decision rules inducted by rough set models are unacceptable as laws to classify new ob- jects. Approkimate decision rules and partial matching rules are proposed to overcome this problem. This paper dis- cusses two typical algorithms for the generation of approximate rules and comparatively analyzes their performance as proven by one case study. Furthermore, one more efficient algorithm is developed based on the two algorithms. This paper also describes the general measures used for matching rules,and a set of formulae are defined for complete matching and partial matching of decision rules according to dependency coefficient in rough set theory. The experi- ments show that the proposed approximation algorithm and measures for matching rules can further improve the matching possibility and correctness of basic decision rules generated based on rough set theory.
Rough Set Approximations and Information Granules

XU You-Hong,ZHU Ding-Hong,

计算机科学 , 2008,
Abstract: In this paper,we prove that basic information granules in Pawlak information systems,incomplete information systems,and incomplete fuzzy information systems can be respectively described as three types of rough set approximations. By using the upper approximations of attribute value of an object with respect to the object-attribute approximation space,we can obtain an object set in which all the objects have the same or similar information,that is,we can transform the object-attribute value into basic infor...
Constructive Research of the Generalized Interval-valued Fuzzy Rough Approximation Operators

XUE Zhan-ao,CEN FengWEI Li-pingHE Hua-can,

计算机科学 , 2009,
Abstract: New lower and upper approximation operators of generalized fuzzy rough sets were constructed and extended their definition to interval.These operators were proved to be equivalent to generalized interval dubois fuzzy rough app-roximation operators in a generalized approximation space formed by any interval-valued fuzzy binary relations.Typical properties of these operators were discussed based on generalized interval-valued fuzzy binary relation.
Improved C4.5 decision trees algorithm based on variable precision rough set

LIU Xing-wen,WANG Dian-hong,CHEN Fen-xiong,

计算机应用研究 , 2011,
Abstract: Aiming at the problems of complexisity and relatively low classification accuracy of decision trees constructed by C4.5 algorithm, this paper proposed a new decision trees classification algorithm (VPRSC4.5) based on the variable precision rough set (VPRS), which took the approximate quality of classification as the heuristic function in order to alleviate the effect of noise data on choosing splitting attributes. It also gave out the solution to the problem how to choose the best attributes as the node when two or more attributes had the same value of approximate quality of classification. Experiments prove that the size and classification accuracy of the decision trees generated by the improved algorithm is superior to the C4.5 algorithm.
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