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Two-Layer Information Granulation:Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction
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作者 Changshun Liu Yan Liu +1 位作者 Jingjing Song Taihua Xu 《Intelligent Automation & Soft Computing》 SCIE 2023年第8期2059-2075,共17页
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
关键词 Attribute reduction information granulation mapping-equiva-lence relation neighborhood rough set
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Granular Computing for Data Analytics:A Manifesto of Human-Centric Computing 被引量:12
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作者 Witold Pedrycz 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第6期1025-1034,共10页
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
关键词 AGGREGATION CLUSTERING design of information granules fuzzy sets granular computing information granules principle of justifiable granularity.
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Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM 被引量:3
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作者 TANG Min-an ZHANG Kai LIU Xing 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第1期32-41,共10页
In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fu... In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future. 展开更多
关键词 urban rail transit passenger flow forecast least squares support vector machine(LS-SVM) fuzzy information granulation chaos particle swarm optimization(CPSO)
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Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index 被引量:1
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作者 Xiangyan Tang Liang Wang +2 位作者 Jieren Cheng Jing Chen Victor S.Sheng 《Computers, Materials & Continua》 SCIE EI 2019年第2期463-491,共29页
The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid mode... The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space. 展开更多
关键词 Data analysis producer price index fuzzy information granulation ARIMA model support vector model.
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Rough-Granular Computing 被引量:3
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作者 Andrzej Skowron 《南昌工程学院学报》 CAS 2006年第2期8-14,共7页
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
关键词 information granulation information granules rough sets granular computing adaptive concept approximation rough-granular computing
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Machine intelligence,rough sets and rough-fuzzy granular computing:uncertainty handling in bio-informatics and Web intelligence
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作者 Sankar K Pal 《重庆邮电大学学报(自然科学版)》 北大核心 2010年第6期720-723,760,共5页
Machine intelligence,is out of the system by the artificial intelligence shown.It is usually achieved by the average computer intelligence.Rough sets and Information Granules in uncertainty management and soft computi... Machine intelligence,is out of the system by the artificial intelligence shown.It is usually achieved by the average computer intelligence.Rough sets and Information Granules in uncertainty management and soft computing and granular computing is widely used in many fields,such as in protein sequence analysis and biobasis determination,TSM and Web service classification Etc. 展开更多
关键词 machine intelligence rough sets information granules rough-fuzzy case generation protein sequence analysis and biobasis determination TSM web service classification
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Information granules and entropy theory in information systems 被引量:41
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作者 LIANG JiYe & QIAN YuHua Key Laboratory of Computational Intelligence and Chinese Information Processing,Ministry of Education School of Computer & Information Technology,Shanxi University,Taiyuan 030006,China 《Science in China(Series F)》 2008年第10期1427-1444,共18页
Information granulation and entropy theory are two main approaches to research uncertainty of an information system, which have been widely applied in many practical issues. In this paper, the characterizations and re... Information granulation and entropy theory are two main approaches to research uncertainty of an information system, which have been widely applied in many practical issues. In this paper, the characterizations and representations of information granules under various binary relations are investigated in information systems, an axiom definition of information granulation is presented, and some existing definitions of information granulation become its special forms. Entropy theory in information systems is further developed and the granulation monotonicity of each of them is proved. Moreover, the complement relationship between information granulation and entropy is established. This investigation unifies the results of measures for uncertainties in complete information systems and incomplete information systems. 展开更多
关键词 information systems information granule information granulation ENTROPY rough set
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Constraint-Based Fuzzy Models for an Environment with Heterogeneous Information-Granules 被引量:2
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作者 赖国华 江义渊 《Journal of Computer Science & Technology》 SCIE EI CSCD 2006年第3期401-411,共11页
A novel framework for fuzzy modeling and model-based control design is described. Based on the theory of fuzzy constraint processing, the fuzzy model can be viewed as a generalized Takagi-Sugeno (TS) fuzzy model wit... A novel framework for fuzzy modeling and model-based control design is described. Based on the theory of fuzzy constraint processing, the fuzzy model can be viewed as a generalized Takagi-Sugeno (TS) fuzzy model with fuzzy functional consequences. It uses multivariate antecedent membership functions obtained by granular-prototype fuzzy clustering methods and consequent fuzzy equations obtained by fuzzy regression techniques. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. The fuzzy-constraint-based approach provides the following features. 1) The knowledge base of a constraint-based fuzzy model can incorporate information with various types of fuzzy predicates. Consequently, it is easy to provide a fusion of different types of knowledge. The knowledge can be from data-driven approaches and/or from controlrelevant physical models. 2) A corresponding inference mechanism for the proposed model can deal with heterogeneous information granules. 3) Both numerical and linguistic inputs can be accepted for predicting new outputs. The proposed techniques are demonstrated by means of two examples: a nonlinear function-fitting problem and the well-known Box-Jenkins gas furnace process. The first example shows that the proposed model uses fewer fuzzy predicates achieving similar results with the traditional rule-based approach, while the second shows the performance can be significantly improved when the control-relevant constraints are considered. 展开更多
关键词 computing with words constraint-based problem solving fuzzy modeling granular computing information granulation
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Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing 被引量:4
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作者 LIAO Weihua JIANG Weiguo HUANG Ziqian 《Journal of Geographical Sciences》 SCIE CSCD 2022年第9期1813-1830,共18页
Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation.The remote sensing ecological index(RSEI)model of the Guangxi Beibu Gulf Economic Zone(GBGEZ)during 2001-2020 was establi... Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation.The remote sensing ecological index(RSEI)model of the Guangxi Beibu Gulf Economic Zone(GBGEZ)during 2001-2020 was established and evaluated using four indices:dryness,wetness,greenness,and heat.This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing.We found that:(1)From 2001 to 2020,the eco-environmental quality(EEQ)of GBGEZ tended to improve,and the spatial difference tended to expand.The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas,and the EEQ in the east and west is better than that in the middle.The contribution of greenness,wetness,and dryness to the improvement of EEQ in the study region increased year by year.(2)From 2001to 2020,the order of the contribution of the EEQ index in the GBGEZ was dryness,wetness,greenness,and heat.(3)The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ. 展开更多
关键词 remote sensing eco-environment spatiotemporal change remote sensing information granules remote sensing information granulation Guangxi Beibu Gulf Economic Zone
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Pattern classification using fuzzy relation and genetic algorithm
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作者 Kumar S.Ray 《International Journal of Intelligent Computing and Cybernetics》 EI 2012年第4期533-565,共33页
Purpose–This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus(FRC)and genetic algorithm(GA).Design/methodology/approach–The paper introduc... Purpose–This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus(FRC)and genetic algorithm(GA).Design/methodology/approach–The paper introduces a new interpretation of multidimensional fuzzy implication(MFI)to represent the author’s knowledge about the training data set.It also considers the notion of a fuzzy pattern vector(FPV)to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space.The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one-dimensional fuzzy implication.For the estimation of Ri floating point representation of GA is used.Thus,a set of fuzzy relations is formed from the new interpretation of MFI.This set of fuzzy relations is termed as the core of the pattern classifier.Once the classifier is constructed the non-fuzzy features of a test pattern can be classified.Findings–The performance of the proposed scheme is tested on synthetic data.Subsequently,the paper uses the proposed scheme for the vowel classification problem of an Indian language.In all these case studies the recognition score of the proposed method is very good.Finally,a benchmark of performance is established by considering Multilayer Perceptron(MLP),Support Vector Machine(SVM)and the proposed method.The Abalone,Hosse colic and Pima Indians data sets,obtained from UCL database repository are used for the said benchmark study.The benchmark study also establishes the superiority of the proposed method.Originality/value–This new soft computing approach to pattern classification is based on a new interpretation of MFI and a novel notion of FPV.A set of fuzzy relations which is the core of the pattern classifier,is estimated using floating point GA and very effective classification of patterns under vague and imprecise environment is performed.This new approach to pattern classification avoids the curse of high dimensionality of feature vector.It can provide multiple classifications under overlapped classes. 展开更多
关键词 Pattern classifier Multidimensional fuzzy implication Fuzzy information granule Fuzzy patter vector Fuzzy relational calculus Genetic algorithms Fuzzy logic Pattern recognition
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