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Fuzzy rough approximations of process data

Alicja Mieszkowicz-Rolka, Leszek Rolka
2008 International Journal of Approximate Reasoning  
Crucial notions of the VPFRS model are redefined and explained. A new way of determining the upper variable precision fuzzy rough approximation is proposed.  ...  As a new aspect, a unified form of expressing the lower and upper crisp approximations is considered. It can be applied to defining new fuzzy rough set models.  ...  Based on process data, the classical control theory tries to create a mathematical model of the human operator.  ... 
doi:10.1016/j.ijar.2007.03.016 fatcat:5e3jwx5kuzhrlckgq7ewoj4j2u

Overview on the Using Rough Set Theory on GIS Spatial Relationships Constraint

Li Jing, Zhou Wenwen
2016 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
This paper analyzes the basic math in recent years in the research of rough set theory and related nature, discusses the GIS uncertainty covering approximation space, covering rough sets, analysis of it  ...  And in the past, many GIS rough applications are based on the equivalence partition pawlak rough set.  ...  With the further increasing of GIS data processing requirements, rough set theory is widely used to spatial data processing, at the same time, it will promote the development of the future GIS data processing  ... 
doi:10.14569/ijarai.2016.050603 fatcat:e2b7s6oozfetfifqo5achv6wo4

Robust function approximation based on fuzzy sets and rough sets

Chih-Ching Hsiao
2009 2009 IEEE International Conference on Fuzzy Systems  
Fuzzy set and the rough set theories turned out to be particularly adequate for the analysis of various types of data, especially, when dealing with inexact, uncertain or vague knowledge.  ...  In this paper, we propose an novel algorithm, which termed as Rough-Fuzzy C-regression model (RFCRM), that define fuzzy subspaces in a fuzzy regression manner and also include Rough-set theory for TSK  ...  Rough sets The rough set theory is a mathematical theory dealing with uncertainty in data. Rough sets rely on the notion of lower and upper approximations of a set.  ... 
doi:10.1109/fuzzy.2009.5277427 dblp:conf/fuzzIEEE/Hsiao09 fatcat:bcbes6xrtfdrndhtd5utsfe5tu

A Rough-set-based for fuzzy modeling with outlier

Chih-Ching Hsiao
2008 2008 SICE Annual Conference  
Fuzzy set and the rough set theories turned out to be particularly adequate for the analysis of various types of data, especially, when dealing with inexact, uncertain or vague knowledge.  ...  Fuzzy-rule-based modeling is a suitable tool that combines good approximation properties with a certain degree of inspects ability.  ...  Rough sets rely on the notion of lower and upper approximations of a set.  ... 
doi:10.1109/sice.2008.4654681 fatcat:txtz5asn65d43hncyr4syq7jee

Guest Editorial for the Special Issue on Fuzzy Rough Sets for Big Data

Weiping Ding, Witold Pedrycz, Chin-Teng Lin
2020 IEEE transactions on fuzzy systems  
co-evolution for fuzzy attribute reduction by quantum leaping PSO with nearest-neighbor memeplexes," IEEE Trans.  ...  He is an Editor-in-Chief for Information Sciences, and an Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley).  ...  He is the recipient of the IEEE Canada Computer Engineering Medal, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.  ... 
doi:10.1109/tfuzz.2020.2979204 fatcat:kau4a4xp2zfdtjbrx5jpfmg7vm

A framework of adaptive T-S type Rough-Fuzzy Inference Systems (ARFIS)

ChangSu Lee, Anthony Zaknich, Thomas Braunl
2008 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)  
Fuzzy inference systems (FIS) are information processing systems using fuzzy logic mechanism to represent the human reasoning process and to make decisions based on uncertain, imprecise environments in  ...  The design of FIS comes from either the experience of human experts in the corresponding field of research or input and output data observations collected from operations of systems.  ...  Fuzzy Inference Process for the T-S type Fuzzy Model Using the designed membership functions and fuzzy rules, the fuzzy inference process in the T-S type model can approximate the unknown input data.  ... 
doi:10.1109/fuzzy.2008.4630425 dblp:conf/fuzzIEEE/LeeZB08 fatcat:gchnnldyrvdkvafib3u7ihi3hq

Fuzzy Rough Sets And Its Application In Data Mining Field

Megha Kumar, Nidhika Yadav
2015 Zenodo  
The theory provides a practical approach for extraction of valid rules fromdata.This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain  ...  and vague data so as to reach at meaningful conclusions.  ...  FUZZY ROUGH SETS A fuzzy-rough set is a generalisation of a rough set, derived from the approximation of a fuzzy set in a crisp approximation space.  ... 
doi:10.5281/zenodo.34822 fatcat:trxorjgmlraldiptpkr6ks6mte

A method for extracting rules from spatial data based on rough fuzzy sets

Hexiang Bai, Yong Ge, Jinfeng Wang, Deyu Li, Yilan Liao, Xiaoying Zheng
2014 Knowledge-Based Systems  
The situation of fuzzy decisions, which is often encountered in spatial data, is beyond the capability of classical rough set theory.  ...  This paper presents a model based on rough fuzzy sets to extract spatial fuzzy decision rules from spatial data that simultaneously have two types of uncertainties, roughness and fuzziness.  ...  Although rough fuzzy sets are special cases of fuzzy rough sets, the modeling process of fuzzy rough sets needs the fuzzification of conditional attributes.  ... 
doi:10.1016/j.knosys.2013.12.008 fatcat:s2xkcjrj35cjtfm5wkww7tla6m

A Rough Set based Fuzzy Inference System for Mining Temporal Medical Databases

U Keerthika
2012 International Journal of Soft Computing  
The goals are pre-processing for feature selection, construction of classifier, and rule induction based on increment rough set approach. The features are selected using Hybrid Genetic Algorithm.  ...  The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in medical databases.  ...  So, the fuzzy temporal rough sets in combination with the genetic algorithm are used in pre-processing the data. E.C.C.Tsang et al [13] focuses on the attributes reduction with fuzzy rough sets.  ... 
doi:10.5121/ijsc.2012.3304 fatcat:rbx6envavfeptorcwd42tl3744

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

Rana Aamir Raza
2021 Lahore Garrison University research journal of computer science and information technology  
In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data.  ...  In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model.  ...  A minor difference between FRST and rough fuzzy set is that the approximation of the fuzzy set in crisp approximation space is called rough fuzzy set and the approximate the crisp set in fuzzy approximation  ... 
doi:10.54692/lgurjcsit.2021.0503224 fatcat:jcweew3hzbfelnwc2vncjucqr4

Generalizations of Rough Sets: From Crisp to Fuzzy Cases [chapter]

Masahiro Inuiguchi
2004 Lecture Notes in Computer Science  
p. 523 Detection of Differences between Syntactic and Semantic Similarities p. 529 Rough Sets and Neural Network Processing of Musical Data Employing Rough Sets and Artificial Neural Networks p  ...  The Ordered Set of Rough Sets p. 49 A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysisp. 59 Structure of Rough Approximations Based on Molecular Lattices p. 69  ... 
doi:10.1007/978-3-540-25929-9_3 fatcat:saiacsrpovgphlh5zzhoq4tcrq

A Knowledge Mining Model for Ranking Institutions using Rough Computing with Ordering Rules and Formal Concept analysis [article]

D. P. Acharjya, L. Ezhilarasi
2011 arXiv   pre-print
In pre process we use rough set on intuitionistic fuzzy approximation space with ordering rules for finding the knowledge whereas in post process we use formal concept analysis to explore better knowledge  ...  To handle such imprecise data certain mathematical tools of greater importance has developed by researches in recent past namely fuzzy set, intuitionistic fuzzy set, rough Set, formal concept analysis  ...  Further rough set is generalized to rough sets on fuzzy approximation space [9] , rough sets on intuitionistic fuzzy approximation spaces [1] .  ... 
arXiv:1108.1986v1 fatcat:vxswq5d7wzg2dmebnhwwxobgwe

Hill-climber based fuzzy-rough feature extraction with an application to cancer classification

Sujata Dash
2013 13th International Conference on Hybrid Intelligent Systems (HIS 2013)  
Hill-climber based fuzzy-rough boundary region generates fuzzy decision reducts, which represent the minimal set of non-redundant features, capable of discerning between all objects.  ...  But discretization of data makes the dataset inconsistent by loosing information.  ...  Both of them are complementary to each other and can be encountered in real-life problems. A fuzzy-rough set is an approximation of a crisp set or a fuzzy set in a fuzzy approximation space.  ... 
doi:10.1109/his.2013.6920499 dblp:conf/his/Dash13 fatcat:tsmuzwnkujemxaekwwj4pp2wtm

A Comparative Study of Color Image Segmentation Using Hard, Fuzzy,Rough Set Based Clustering Techniques

Venkateswara Reddy Eluri, Dr. E.S. Reddy
rough sets and Fuzzy C-Means Method, and also compare the effectiveness of the clustering methods such as Hard C Means (HCM), Fuzzy C Means (FCM), Fuzzy K Means (FKM), Rough C Means (RCM) with cluster  ...  Image segmentation is the process of subdividing an image into its constituent parts and extracting these parts of interest, which are the objects.  ...  In each iteration, cluster centers are updated and data point is assigned to lower approximations or upper approximation of a cluster. This process is repeated until convergence criterion is met.  ... 
doi:10.24297/ijct.v11i8.3005 fatcat:xbgr47t7vfa5bnukvafifrb7kq


Raval Dhwani Jayant .
2014 International Journal of Research in Engineering and Technology  
In today's world everything is done digitally and so we have lots of data raw data. This data are useful to predict future events if we proper use it.  ...  In this paper we have shown what is the problem with clustering categorical data with rough set and who we can overcome with improvement. ---  ...  of neural networks, fuzzy logic, genetic algorithms, and rough sets and thus automating the process of information gathering.  ... 
doi:10.15623/ijret.2014.0311030 fatcat:ur54nhrzqrh4vjunst6doa7j6m
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