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Hierarchical fuzzy configuration of implementation strategies

Angela Sodan, Vicenç Torra
1999 Proceedings of the 1999 ACM symposium on Applied computing - SAC '99  
Already many software systems offer multiple specialized implementation strategies and substrategies, differing in terms of applicability and/or cost, depending on the ap plication context.  ...  We present a rule-based approach, integrating fuzziness for the classification of application characteristics and for gradual selection preference in rules.  ...  influence each other in terms of performance.  ... 
doi:10.1145/298151.298343 dblp:conf/sac/SodanT99 fatcat:pycrnvug3beaja4dleauqdmstq

Granular Mining and Rough-Fuzzy Pattern Recognition: A Way to Natural Computation

Sankar K. Pal
2012 The IEEE intelligent informatics bulletin  
Bose Fellowship of the Govt. of India, as well as the technical support of Mr. Avatharam Ganivada.  ...  The significance of features varies with the granularity levels. Accordingly, the NRS based algorithm selects different feature subsets with the change offunction and Φ value.  ...  It is demonstrated that incorporation of the concept of granularity in reflecting the rough resemblance in nearby gray levels and pixels improves the performance over fuzzy set theoretic segmentation.  ... 
dblp:journals/cib/Pal12 fatcat:rcenhthsqrfibj2ppuarlkjafq

From Fuzzy Models to Granular Fuzzy Models [chapter]

Witold Pedrycz
2011 Lecture Notes in Computer Science  
In this study, we offer a general view at the area of fuzzy modeling and elaborate on a new direction of system modeling by introducing a concept of granular models.  ...  Those models constitute a generalization of existing fuzzy models and, in contrast to existing models, generate results in the form of information granules (such as intervals, fuzzy sets, rough sets and  ...  The first category of spaces associates with a construction of granular fuzzy models whereas the second one help establish functioning of fuzzy models in the presence of granular data.  ... 
doi:10.1007/978-3-642-23713-3_10 fatcat:q5i35fiv4baobeow4pseukwu5a

A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems [chapter]

O. Cordón, F. Herrera, M.J. del Jesus, L. Magdalena, A.M. Sánchez, P. Villar
2003 Intelligent Systems for Information Processing  
In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy Rule-Based Classification System (FRBCS) and to automatically learn the whole Data Base definition.  ...  An FRBCS is an automatic classification system that uses fuzzy rules as knowledge representation tool. Two different components are distinguished within it: 1. The KB, composed of:  ...  This operation mode makes the granularity and fuzzy set definitions have a significant influence on the FRBCS performance.  ... 
doi:10.1016/b978-044451379-3/50026-1 fatcat:hujxjp4dirbddoubtn66ud4x5i

Vehicle Accident Severity Rules Mining Using Fuzzy Granular Decision Tree [chapter]

Hamid Kiavarz Moghaddam, Xin Wang
2014 Lecture Notes in Computer Science  
This thesis proposes a new fuzzy granular decision tree to generate road collision rules to apply to the discrete and continuous data stored in collision databases.  ...  To improve the efficiency of the algorithm, the fuzzy rough set feature selection is applied .The major highways in California are considered as a case study to examine the proposed approach.  ...  CF2}; therefore, CF3 was added to selected features set in the second level. After CF1 was added to CF2 and CF3 in the third level, the value of dependency did not increase.  ... 
doi:10.1007/978-3-319-08644-6_29 fatcat:mcwspl6l4zaobozeikngkeziai

A new integrated fuzzy MCDM approach and its application to wastewater management

Mehtap Dursun
2018 International Journal of Intelligent Systems and Applications in Engineering  
The weights of the criteria are calculated employing DEMATEL method, which enables to consider inner dependencies among criteria. Then, fuzzy TOPSIS method is utilized to rank the alternatives.  ...  The multi-granular linguistic information obtained from decision-makers are unified and aggregated employing linguistic hierarchies and 2-tuple fuzzy linguistic representation model.  ...  The transformation function between linguistic terms in any level of the hierarchy is defined as Level 1 Level 2 Level 3 Level 4 ( ) ( ) t n t l , l(1, t n t l , l(1, ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (  ... 
doi:10.18201/ijisae.2018634723 fatcat:7nkgmnsb3vgsjkokjyfd4mwjfi

Data science, big data and granular mining

Sankar K. Pal, Saroj K. Meher, Andrzej Skowron
2015 Pattern Recognition Letters  
In the recent past, evolution of research interest has cropped up a relatively new area called, granular computing (GrC), due to the need and challenges from various domains of applications, such  ...  Depending on the size and shape, and with a certain level of granularity, the granules may characterize a specific aspect of the problem.  ...  Depending on the problems and whether the granules and the process are fuzzy or crisp, one may have operations like granular fuzzy computing or fuzzy granular computing.  ... 
doi:10.1016/j.patrec.2015.08.001 fatcat:32pup546rbhtbdsk33kxry6yvq

Fuzzy granular gravitational clustering algorithm

Mauricio A. Sanchez, Oscar Castillo, Juan R. Castro, Antonio Rodriguez-Diaz
2012 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)  
Given the nature of clustering algorithms of finding automatically, or semi-automatically, an unspecified number of clusters, much work has been done in this area.  ...  Two examples of datasets are compared, one synthetic and one of the Iris, are benchmarked against the fuzzy subtractive algorithm.  ...  ACKNOWLEDGMENT We thank the MyDCI program of the Division of Graduate Studies and Research, UABC, and the financial support provided by our sponsor CONACYT contract grant number: 314258.  ... 
doi:10.1109/nafips.2012.6291052 fatcat:yzu6lj2bdrhgxlosdnzhxxjpnq

A genetic learning of the fuzzy rule-based classification system granularity for highly imbalanced data-sets

Pedro Villar, Alberto Fernandez, Francisco Herrera
2009 2009 IEEE International Conference on Fuzzy Systems  
In this contribution we analyse the significance of the granularity level (number of labels) in Fuzzy Rule-Based Classification Systems in the scenario of data-sets with a high imbalance degree.  ...  The aim of this work is to adapt the number of fuzzy labels for each problem, applying a fine granularity in those variables which have a higher dispersion of values and a thick granularity in the variables  ...  ACKNOWLEDGMENT This work had been supported by the Spanish Ministry of Science and Technology under Project TIN2008-06681-C06-01.  ... 
doi:10.1109/fuzzy.2009.5277304 dblp:conf/fuzzIEEE/VillarFH09 fatcat:nawkin3ej5hzbgko35zcyiugqy

Incremental Granular Fuzzy Modeling Using Imprecise Data Streams [chapter]

Daniel Leite, Fernando Gomide
2015 Studies in Fuzziness and Soft Computing  
Fuzzy granular computing 35 [4-6] hypothesizes that accepting some level of imprecision may be beneficial and 36 therefore suggests a balance between precision and uncertainty. 37 Linguistic and functional  ...  Linguistic and functional rule-based systems have been used in granular 43 data modeling [8, 9]. 44 This chapter addresses system modeling using streams of fuzzy interval data.  ...  Different values of produce different representations of the same 188 data set in different levels of granularities. For normalized data, assumes values in 189 [0, 1].  ... 
doi:10.1007/978-3-319-19683-1_7 fatcat:pxpv5lglyfbqpchwtg56bmsbku

Perception based functions with boundary conditions

Ildar Z. Batyrshin
2003 European Society for Fuzzy Logic and Technology  
Fuzzy functions reconstructed from such description may be considered as solutions of correspondent differential equation.  ...  The methods of reconstruction of perception based functions (PBF) given by the set of rules like "If X is SMALL then Y is VERY QUICKLY INCREASING", "If X is BETWEEN A and B then Y is SLOWLY DECREASING"  ...  Acknowledgements Research supported in part by IMP project CDI.00006 and RFBR Grant 02-01-00092.  ... 
dblp:conf/eusflat/Batyrshin03 fatcat:nu5jkwhqxvfofhdcwk63jf3kpa

Interval-based parameters for stress diffusion in granular medium [chapter]

D. Boumezerane
2018 Safety and Reliability – Safe Societies in a Changing World  
A point load applied on the surface of a granular media will follow an erratic path, depending on the probability of transition between the grains.  ...  According to Bourdeau (1986) , diffusion of stresses in a granular medium can be described using a probabilistic approach.  ...  The triangular fuzzy number for K can be written in terms of intervals at level α, K Vertical stress distribution under axis for different level cuts of K.  ... 
doi:10.1201/9781351174664-329 fatcat:okzhjn6rrfgjfb4r756ypox4pm

Fuzzy granular evolving modeling for time series prediction

Daniel Leite, Fernando Gomide, Rosangela Ballini, Pyramo Costa
2011 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)  
A profusion of systems and algorithms devoted to this end has been constructed under the conceptual framework of granular computing.  ...  This paper outlines a fuzzy set based granular evolving modeling -FBeM -approach for learning from imprecise data. Granulation arises because modeling uncertain data dispenses attention to details.  ...  ACKNOWLEDGMENT The first author acknowledges CAPES, Brazilian Ministry of Education, for his fellowship.  ... 
doi:10.1109/fuzzy.2011.6007452 dblp:conf/fuzzIEEE/LeiteGBC11 fatcat:clvfnlcdcnd2zpsuabxzlotmoa

Performance Analysis of Granular versus Traditional Neural Network Classifiers: Preliminary Results

Gerardo Felix, Gonzalo Npoles, Rafael Falcon, Rafael Bello, Koen Vanhoof
2018 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)  
As a bigger picture, this study paves the way for conducting a more thorough evaluation of granular versus low-level neural classifiers in the near future.  ...  We want to challenge this belief by exploring the performance of a recently introduced granular classifier termed Fuzzy-Rough Cognitive Networks against low-level (i.e., traditional) neural networks.  ...  numerical (not granular) level.  ... 
doi:10.1109/civemsa.2018.8439971 fatcat:a5wwenl3vbfbppu2kon4h4pcba

How Much Is "About"? Fuzzy Interpretation of Approximate Numerical Expressions [chapter]

Sébastien Lefort, Marie-Jeanne Lesot, Elisabetta Zibetti, Charles Tijus, Marcin Detyniecki
2016 Communications in Computer and Information Science  
membership functions.  ...  This paper proposes to interpret ANEs as fuzzy numbers.  ...  This approach has the advantage of taking into account the ANE granularity; however, it does not address the issue of the relative magnitude: all ANEs at the same granularity level result in the same interval  ... 
doi:10.1007/978-3-319-40596-4_20 fatcat:5qyhnv74ujahta23ggf66biv3m
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