Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from scholars.Information granulation is a key step of attribute reduction,and its efficiency has a significa...Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from scholars.Information granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute reduction.The information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is inefficient.To fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information granulation.The first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence classes.The second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information granulation.Experimental results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the system.The strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.展开更多
In this paper, some important issues of granularity are discussed mainly in information systems (ISs) based on binary relation. Firstly, the vector representation method of knowledge granules is proposed in an infor-m...In this paper, some important issues of granularity are discussed mainly in information systems (ISs) based on binary relation. Firstly, the vector representation method of knowledge granules is proposed in an infor-mation system based on binary relation to eliminate limitations of set representation method. Secondly, operators among knowledge granularity are introduced and some important properties of them are studied carefully. Thirdly, distance between two knowledge granules is established and granular space is constructed based on it. Fourthly, axiomatic definition of knowledge granularity is investigated, and one can find that some existed knowledge granularities are special cases under the definition. In addition, as an application of knowledge granular space, an example is employed to validate some results in our work.展开更多
Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a n...Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.展开更多
Accuracy and roughness, proposed by Pawlak(1982), might draw a conclusion inconsistent with our intuition in some cases. This letter analyzes the limitations in these measures and proposes improved accuracy and roughn...Accuracy and roughness, proposed by Pawlak(1982), might draw a conclusion inconsistent with our intuition in some cases. This letter analyzes the limitations in these measures and proposes improved accuracy and roughness measures based on information theory.展开更多
The concept of Rough sets was first introduced by Pawlak in 1981. Because this theory is very important and useful in many fields, e. g. in the information systems and the man-machine systems, many scholars both at ho...The concept of Rough sets was first introduced by Pawlak in 1981. Because this theory is very important and useful in many fields, e. g. in the information systems and the man-machine systems, many scholars both at home and abroad have studied it and obtained a lot of results. Now the theory of Rough sets is still progressing rapidly. However, when this theory is applied to describing an information system, the indiscernibility展开更多
To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a ...To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.展开更多
基金supported by the National Natural Science Foundation of China (Nos.62006099,62076111)the Key Laboratory of Oceanographic Big Data Mining&Application of Zhejiang Province (No.OBDMA202104).
文摘Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from scholars.Information granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute reduction.The information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is inefficient.To fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information granulation.The first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence classes.The second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information granulation.Experimental results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the system.The strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.
文摘In this paper, some important issues of granularity are discussed mainly in information systems (ISs) based on binary relation. Firstly, the vector representation method of knowledge granules is proposed in an infor-mation system based on binary relation to eliminate limitations of set representation method. Secondly, operators among knowledge granularity are introduced and some important properties of them are studied carefully. Thirdly, distance between two knowledge granules is established and granular space is constructed based on it. Fourthly, axiomatic definition of knowledge granularity is investigated, and one can find that some existed knowledge granularities are special cases under the definition. In addition, as an application of knowledge granular space, an example is employed to validate some results in our work.
文摘Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.
基金National Natural Science Foundation of China(60073012)Natural Sceience Foundation of Jiangsu, China(BK2001004)Visiting Scholar Foundation of Key Lab in Wuhan University
文摘Accuracy and roughness, proposed by Pawlak(1982), might draw a conclusion inconsistent with our intuition in some cases. This letter analyzes the limitations in these measures and proposes improved accuracy and roughness measures based on information theory.
基金Acknowledgments： The work was supported in part by the National Science Foundation of China （No. 70571032） and the Scientific Research Foundation of Hunan Provincial Education Department （No. 06C367）.
文摘The concept of Rough sets was first introduced by Pawlak in 1981. Because this theory is very important and useful in many fields, e. g. in the information systems and the man-machine systems, many scholars both at home and abroad have studied it and obtained a lot of results. Now the theory of Rough sets is still progressing rapidly. However, when this theory is applied to describing an information system, the indiscernibility
基金This work was supported by the National Natural Science Foundation of China （Grant Nos. 61170165, 61100116, 61272419, 61373062）, Natural Science Foundation of Jiangsu Province of China （BK2011492, BK2012700, BK20130471）, Qing Lan Project of JiangsuProvince of China, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information （Nanjing University of Science and Tech- nology）, Ministry of Education （30920130122005）, Key Laboratory of Arti- ficial Intelligence of Sichuan Province （2013RYJ03）, Natural Science Foun- dation of Jiangsu Higher Education Institutions of China （13KJB520003, 13KJD520008）.
基金supported by the National Natural Science Foundation of China (61403184, 61105082)the ‘1311 Talent Plan’ of Nanjing University of Posts and Telecommunications (NY2013)+3 种基金the ‘Qinglan’ Project of Jiangsu Province (QL2016)the Natural Science Foundation of Nanjing University of Posts and Telecommunications (215149)the Priority Academic Program Development of Jiangsu Higher Education Institutions, (PAPD)the Major Program of the Natural Science Foundation of Jiangsu Province Education Commission (17KJA120001)
文摘To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No．10671158)甘肃省教育厅科研基金(the Research Foundation of Department of Education of Gansu Province，China under Grant No．0701-16)。
