Accurate classification of encrypted traffic plays an important role in network management.However,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability ...Accurate classification of encrypted traffic plays an important role in network management.However,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability to classify traffic with multi-level features,and degradation due to limited training traffic size.To address these problems,this paper proposes a traffic granularity-based cryptographic traffic classification method,called Granular Classifier(GC).In this paper,a novel Cardinality-based Constrained Fuzzy C-Means(CCFCM)clustering algorithm is proposed to address the problem caused by limited training traffic,considering the ratio of cardinality that must be linked between flows to achieve good traffic partitioning.Then,an original representation format of traffic is presented based on granular computing,named Traffic Granules(TG),to accurately describe traffic structure by catching the dispersion of different traffic features.Each granule is a compact set of similar data with a refined boundary by excluding outliers.Based on TG,GC is constructed to perform traffic classification based on multi-level features.The performance of the GC is evaluated based on real-world encrypted network traffic data.Experimental results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.展开更多
In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between ...In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.展开更多
As an emerging field of study, granular computing has received much attention. Many models, frameworks, methods and techniques have been proposed and studied. It is perhaps the time to seek for a general and unified v...As an emerging field of study, granular computing has received much attention. Many models, frameworks, methods and techniques have been proposed and studied. It is perhaps the time to seek for a general and unified view so that fundamental issues can be examined and clarified.This paper examines granular computing from three perspectives.By viewing granular computing as a way of structured thinking,we focus on its philosophical foundations in modeling human perception of the reality.By viewing granular computing as a method of structured problem solving,we examine its theoretical and methodological foundations in solving a wide range of real-world problems.By viewing granular computing as a paradigm of information processing,we turn our attention to its more concrete techniques. The three perspectives together offer a holistic view of granular computing.展开更多
Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Comput...Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Computing(GC). We discuss the Rough-Granular Computing(RGC) approach to modeling of computations in complex adaptive systems and multiagent systems as well as for approximate reasoning about the behavior of such systems. The RGC methods have been successfully applied for solving complex problems in areas such as identification of objects or behavioral patterns by autonomous systems, web mining, and sensor fusion.展开更多
Granular Computing on partitions(RST),coverings(GrCC) and neighborhood systems(LNS) are examined: (1) The order of generality is RST, GrCC, and then LNS. (2) The quotient structure: In RST, it is called quotient set. ...Granular Computing on partitions(RST),coverings(GrCC) and neighborhood systems(LNS) are examined: (1) The order of generality is RST, GrCC, and then LNS. (2) The quotient structure: In RST, it is called quotient set. In GrCC, it is a simplical complex, called the nerve of the covering in combinatorial topology. For LNS, the structure has no known description. (3) The approximation space of RST is a topological space generated by a partition, called a clopen space. For LNS, it is a generalized/pretopological space which is more general than topological space. For GrCC,there are two possibilities. One is a special case of LNS,which is the topological space generated by the covering. There is another topological space, the topology generated by the finite intersections of the members of a covering The first one treats covering as a base, the second one as a subbase. (4) Knowledge representations in RST are symbol-valued systems. In GrCC, they are expression-valued systems. In LNS, they are multivalued system; reported in 1998 . (5) RST and GRCC representation theories are complete in the sense that granular models can be recaptured fully from the knowledge representations.展开更多
This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular compu...This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular computing which are induced by coverings of the given universe.展开更多
This paper has two purposes.One is to present a critical examination of the rise of granular computing and the other is to suggest a triarchic theory of granular computing.By examining the reasons,justifications,and m...This paper has two purposes.One is to present a critical examination of the rise of granular computing and the other is to suggest a triarchic theory of granular computing.By examining the reasons,justifications,and motivations for the rise of granular computing,we may be able to fully appreciate its scope,goal and potential values.The results enable us to formulate a triarchic theory in the light of research results from many disciplines.The three components of the theory are labeled as the philosophy,the methodology,and the computation.The integration of the three offers a unified view of granular computing as a way of structured thinking,a method of structured problem solving,and a paradigm of structured information processing,focusing on hierarchical granular structures.The triarchic theory is an important effort in synthesizing the various theories and models of granular computing.展开更多
Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computin...Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.展开更多
Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simulta...Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.展开更多
In this article,a real number is defined as a granulation and the real space is transformed into real granular space[1].In the entironment,solution of nonlinear equation is denoted by granulation in real granular spac...In this article,a real number is defined as a granulation and the real space is transformed into real granular space[1].In the entironment,solution of nonlinear equation is denoted by granulation in real granular space.Hence,the research of whole optimization to solve nonlinear equation based on granular computing is proposed[2].In classical case,we solve usually accurate solution of problems.If can't get accurate solution,also finding out an approximate solution to close to accurate solution.But in real space,approximate solution to close to accurate solution is very vague concept.In real granular space,all of the approximate solutions to close to accurate solution are constructed a set,it is a granulation in real granular space.Hence,this granulation is an accurate solution to solve problem in some sense,such,we avoid to say vaguely "approximate solution to close to accurate solution".We introduce the concept of granulation in one dimension real space.Any positive real number a together with moving infinite small distance ε will be constructed an interval [a-ε,a+ε],we call it as granulation in real granular space,denoted by ε(a)or [a].We will discuss related properties and operations[3] of the granulations.Let one dimension real space be R,where each real number a will be generated a granulation,hence we get a granular space R based on real space R.Obviously,R∈R.Infinite small number in real space R is only 0,and there are three infinite small granulations in real number granular space R:[0],[ε] and [-ε].As the graph in Fig.1 shows.In Fig.1,[-ε] is a negative infinite small granulation,[ε] is a positive infinite small granulation,[0] is a infinite small granulation.[a] is a granulation of real number a generating,it could be denoted by interval [a-ε,a+ε] in real space [3-5].Fig.1 Real granulations [0] and [a] Let f(x)=0 be a nonlinear equation,its graph in interval [-3,10] is showed in Fig.2.Where-3≤x≤10 Relation ρ(f| |,ε)is defined as follows:(x1,x2)∈ρ(f| |,ε)iff |f(x1)-f(x2)| < ε Where ε is any given small real number.We have five approximate solution sets on the nonlinear equation f(x)=0 by ρ(f| |,ε)∧|f(x)|[a,b]max,to denote by granulations [(xi1+xi2)/2],[(xi3+xi4)/2],[(xi5+xi6)/2],[(xi7+xi8)/2] and [(xi9+xi10)/2] respectively,where |f(x)|[a,b]max denotes local maximum on x∈[a,b].This is whole optimum on nonlinear equation in interval [-3,10].We will get best optimization solution on nonlinear equation via computing f(x)to use the five solutions denoted by granulation in one dimension real granular space[2,5].展开更多
In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing pa...In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing paradigm which covers all the granularity the study of the theory, methods, techniques and the tools. In many areas are the basic ideas of granular computing, such as the interval analysis, rough set theory, clustering analysis and information retrieval, machine learning, database, etc. With the theory of domain known division of target concept and rule acquisition, in knowledge discovery, data mining and the pattern recognition is widely used. Under this basis, in this paper, we propose the fuzzy rough theory based computing paradigm that gains ideal performance.展开更多
The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has b...The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has been vividly compared by the renowned statistician,prof.Friedman of Stanford University,to the advances in human mobility from the period of walking afoot to the era of jet travel.These essential changes in data have brought new challenges to the development of new data mining methods,especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms."Hot" datasets like biomedical,financial or net user behavior data are just a few examples.Mining such temporal or stream data is on the agenda of many research centers and companies worldwide.In the data mining community,there is a rapidly growing interest in developing methods for process mining,e.g.,for discovery of structures of temporal processes from data.Works on process mining have recently been undertaken by many renowned centers worldwide.This research is also related to functional data analysis,cognitive networks,and dynamical system modeling,e.g.,in biology.In the lecture,we outline an approach to discovery of processes from data and domain knowledge which is based on the rough-granular computing.展开更多
The images of granular ore media were captured by X-ray CT scanner. Combined with digital image processing and finite element techniques, the three-dimensional geometrical model, which represents the realistic pore st...The images of granular ore media were captured by X-ray CT scanner. Combined with digital image processing and finite element techniques, the three-dimensional geometrical model, which represents the realistic pore structure of the media, was constructed. With this model, three dimensional pore scale fluid flow among particles was simulated. Then the distributions of fluid flow velocity and pressure were analyzed and the hydraulic conductivity was calculated. The simulation results indicate the fluid flow behaviors are mainly dominated by the volume and topological structure of pore space. There exist obvious preferential flow and leaching blind zones simultaneously in the medium. The highest velocities generally occur in those narrow pores with high pressure drops. The hydraulic conductivity obtained by simulation is the same order of magnitude as the laboratory test result, which denotes the validity of the model. The pore-scale and macro-scale are combined and the established geometrical model can be used for the simulations of other phenomena during heap leaching process.展开更多
粒球邻域粗糙集(Granular Ball Neighborhood Rough Set,GBNRS)作为一种经典的属性约简方法,要求粒球的纯度严格为1,在类边界处会产生大量样本数为1的粒球.这些粒球通常被误判为离群点并剔除,导致类边界信息的丢失.为了解决此问题.文中...粒球邻域粗糙集(Granular Ball Neighborhood Rough Set,GBNRS)作为一种经典的属性约简方法,要求粒球的纯度严格为1,在类边界处会产生大量样本数为1的粒球.这些粒球通常被误判为离群点并剔除,导致类边界信息的丢失.为了解决此问题.文中首先定义模糊纯度函数,融合隶属度与类别标签,作为粒球质量的评价指标.此函数基于动态质量评估和优化策略,综合考虑数据点的隶属度、数据点的类标签及粒球的类标签三重信息.然后,在粒球分裂过程中,引入分类显著性阈值β,自适应调整M-means的m值,构建模糊纯度粒球生成算法.进一步地,针对粗糙集属性约简问题,设计前向属性约简算法,并提出基于模糊纯度粒球的粗糙集模型(Rough Set Model Based on Fuzzy Purity Granular Ball,FPGBRS).最后,在12个真实数据集上的实验表明,FPGBRS可提升分类精度和效率.展开更多
基金supported in part by the Shandong Provincial Natural Science Foundation under Grant ZR2021QF008the National Natural Science Foundation of China under Grant 62072351+1 种基金in part by the open research project of ZheJiang Lab under grant 2021PD0AB01in part by the 111 Project under Grant B16037。
文摘Accurate classification of encrypted traffic plays an important role in network management.However,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability to classify traffic with multi-level features,and degradation due to limited training traffic size.To address these problems,this paper proposes a traffic granularity-based cryptographic traffic classification method,called Granular Classifier(GC).In this paper,a novel Cardinality-based Constrained Fuzzy C-Means(CCFCM)clustering algorithm is proposed to address the problem caused by limited training traffic,considering the ratio of cardinality that must be linked between flows to achieve good traffic partitioning.Then,an original representation format of traffic is presented based on granular computing,named Traffic Granules(TG),to accurately describe traffic structure by catching the dispersion of different traffic features.Each granule is a compact set of similar data with a refined boundary by excluding outliers.Based on TG,GC is constructed to perform traffic classification based on multi-level features.The performance of the GC is evaluated based on real-world encrypted network traffic data.Experimental results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.
文摘In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.
文摘As an emerging field of study, granular computing has received much attention. Many models, frameworks, methods and techniques have been proposed and studied. It is perhaps the time to seek for a general and unified view so that fundamental issues can be examined and clarified.This paper examines granular computing from three perspectives.By viewing granular computing as a way of structured thinking,we focus on its philosophical foundations in modeling human perception of the reality.By viewing granular computing as a method of structured problem solving,we examine its theoretical and methodological foundations in solving a wide range of real-world problems.By viewing granular computing as a paradigm of information processing,we turn our attention to its more concrete techniques. The three perspectives together offer a holistic view of granular computing.
基金The grant3 T11C 00226 from Min istroyf ScientifiRcesearchand InformationTechnologyoftheRepublicofPoland.
文摘Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Computing(GC). We discuss the Rough-Granular Computing(RGC) approach to modeling of computations in complex adaptive systems and multiagent systems as well as for approximate reasoning about the behavior of such systems. The RGC methods have been successfully applied for solving complex problems in areas such as identification of objects or behavioral patterns by autonomous systems, web mining, and sensor fusion.
文摘Granular Computing on partitions(RST),coverings(GrCC) and neighborhood systems(LNS) are examined: (1) The order of generality is RST, GrCC, and then LNS. (2) The quotient structure: In RST, it is called quotient set. In GrCC, it is a simplical complex, called the nerve of the covering in combinatorial topology. For LNS, the structure has no known description. (3) The approximation space of RST is a topological space generated by a partition, called a clopen space. For LNS, it is a generalized/pretopological space which is more general than topological space. For GrCC,there are two possibilities. One is a special case of LNS,which is the topological space generated by the covering. There is another topological space, the topology generated by the finite intersections of the members of a covering The first one treats covering as a base, the second one as a subbase. (4) Knowledge representations in RST are symbol-valued systems. In GrCC, they are expression-valued systems. In LNS, they are multivalued system; reported in 1998 . (5) RST and GRCC representation theories are complete in the sense that granular models can be recaptured fully from the knowledge representations.
文摘This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular computing which are induced by coverings of the given universe.
基金supported by a Discovery grant from NSERC Canada.
文摘This paper has two purposes.One is to present a critical examination of the rise of granular computing and the other is to suggest a triarchic theory of granular computing.By examining the reasons,justifications,and motivations for the rise of granular computing,we may be able to fully appreciate its scope,goal and potential values.The results enable us to formulate a triarchic theory in the light of research results from many disciplines.The three components of the theory are labeled as the philosophy,the methodology,and the computation.The integration of the three offers a unified view of granular computing as a way of structured thinking,a method of structured problem solving,and a paradigm of structured information processing,focusing on hierarchical granular structures.The triarchic theory is an important effort in synthesizing the various theories and models of granular computing.
文摘Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.
基金Supported by Natural Science Foundation of China ( No. 60373061).
文摘Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.
文摘In this article,a real number is defined as a granulation and the real space is transformed into real granular space[1].In the entironment,solution of nonlinear equation is denoted by granulation in real granular space.Hence,the research of whole optimization to solve nonlinear equation based on granular computing is proposed[2].In classical case,we solve usually accurate solution of problems.If can't get accurate solution,also finding out an approximate solution to close to accurate solution.But in real space,approximate solution to close to accurate solution is very vague concept.In real granular space,all of the approximate solutions to close to accurate solution are constructed a set,it is a granulation in real granular space.Hence,this granulation is an accurate solution to solve problem in some sense,such,we avoid to say vaguely "approximate solution to close to accurate solution".We introduce the concept of granulation in one dimension real space.Any positive real number a together with moving infinite small distance ε will be constructed an interval [a-ε,a+ε],we call it as granulation in real granular space,denoted by ε(a)or [a].We will discuss related properties and operations[3] of the granulations.Let one dimension real space be R,where each real number a will be generated a granulation,hence we get a granular space R based on real space R.Obviously,R∈R.Infinite small number in real space R is only 0,and there are three infinite small granulations in real number granular space R:[0],[ε] and [-ε].As the graph in Fig.1 shows.In Fig.1,[-ε] is a negative infinite small granulation,[ε] is a positive infinite small granulation,[0] is a infinite small granulation.[a] is a granulation of real number a generating,it could be denoted by interval [a-ε,a+ε] in real space [3-5].Fig.1 Real granulations [0] and [a] Let f(x)=0 be a nonlinear equation,its graph in interval [-3,10] is showed in Fig.2.Where-3≤x≤10 Relation ρ(f| |,ε)is defined as follows:(x1,x2)∈ρ(f| |,ε)iff |f(x1)-f(x2)| < ε Where ε is any given small real number.We have five approximate solution sets on the nonlinear equation f(x)=0 by ρ(f| |,ε)∧|f(x)|[a,b]max,to denote by granulations [(xi1+xi2)/2],[(xi3+xi4)/2],[(xi5+xi6)/2],[(xi7+xi8)/2] and [(xi9+xi10)/2] respectively,where |f(x)|[a,b]max denotes local maximum on x∈[a,b].This is whole optimum on nonlinear equation in interval [-3,10].We will get best optimization solution on nonlinear equation via computing f(x)to use the five solutions denoted by granulation in one dimension real granular space[2,5].
文摘In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing paradigm which covers all the granularity the study of the theory, methods, techniques and the tools. In many areas are the basic ideas of granular computing, such as the interval analysis, rough set theory, clustering analysis and information retrieval, machine learning, database, etc. With the theory of domain known division of target concept and rule acquisition, in knowledge discovery, data mining and the pattern recognition is widely used. Under this basis, in this paper, we propose the fuzzy rough theory based computing paradigm that gains ideal performance.
基金supported by the grant N N516 368334 from Ministry of Science and Higher Education of the Republic of Poland and by the grant Innovative Economy Operational Programme 2007-2013(Priority Axis 1.Research and development of new technologies)managed by Ministry of Regional Development of the Republic of Poland.
文摘The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has been vividly compared by the renowned statistician,prof.Friedman of Stanford University,to the advances in human mobility from the period of walking afoot to the era of jet travel.These essential changes in data have brought new challenges to the development of new data mining methods,especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms."Hot" datasets like biomedical,financial or net user behavior data are just a few examples.Mining such temporal or stream data is on the agenda of many research centers and companies worldwide.In the data mining community,there is a rapidly growing interest in developing methods for process mining,e.g.,for discovery of structures of temporal processes from data.Works on process mining have recently been undertaken by many renowned centers worldwide.This research is also related to functional data analysis,cognitive networks,and dynamical system modeling,e.g.,in biology.In the lecture,we outline an approach to discovery of processes from data and domain knowledge which is based on the rough-granular computing.
基金Projects (50934002, 51074013, 51104100) supported by the National Natural Science Foundation of China
文摘The images of granular ore media were captured by X-ray CT scanner. Combined with digital image processing and finite element techniques, the three-dimensional geometrical model, which represents the realistic pore structure of the media, was constructed. With this model, three dimensional pore scale fluid flow among particles was simulated. Then the distributions of fluid flow velocity and pressure were analyzed and the hydraulic conductivity was calculated. The simulation results indicate the fluid flow behaviors are mainly dominated by the volume and topological structure of pore space. There exist obvious preferential flow and leaching blind zones simultaneously in the medium. The highest velocities generally occur in those narrow pores with high pressure drops. The hydraulic conductivity obtained by simulation is the same order of magnitude as the laboratory test result, which denotes the validity of the model. The pore-scale and macro-scale are combined and the established geometrical model can be used for the simulations of other phenomena during heap leaching process.
文摘粒球邻域粗糙集(Granular Ball Neighborhood Rough Set,GBNRS)作为一种经典的属性约简方法,要求粒球的纯度严格为1,在类边界处会产生大量样本数为1的粒球.这些粒球通常被误判为离群点并剔除,导致类边界信息的丢失.为了解决此问题.文中首先定义模糊纯度函数,融合隶属度与类别标签,作为粒球质量的评价指标.此函数基于动态质量评估和优化策略,综合考虑数据点的隶属度、数据点的类标签及粒球的类标签三重信息.然后,在粒球分裂过程中,引入分类显著性阈值β,自适应调整M-means的m值,构建模糊纯度粒球生成算法.进一步地,针对粗糙集属性约简问题,设计前向属性约简算法,并提出基于模糊纯度粒球的粗糙集模型(Rough Set Model Based on Fuzzy Purity Granular Ball,FPGBRS).最后,在12个真实数据集上的实验表明,FPGBRS可提升分类精度和效率.
