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Rough–fuzzy functions in classification

Manish Sarkar
2002 Fuzzy sets and systems (Print)  
This paper generalizes the concept of rough membership functions in pattern classiÿcation tasks to rough-fuzzy membership functions and rough-fuzzy ownership functions.  ...  In this paper, various set theoretic properties of the rough-fuzzy functions are exploited to characterize the concept of rough-fuzzy sets.  ...  In absence of the fuzziness in the output class, the rough-fuzzy membership function (rough-fuzzy ownership function) reduces to the original rough membership function (rough ownership function).  ... 
doi:10.1016/s0165-0114(02)00119-7 fatcat:4mqo6xjjqjam7pkp7yzmw6ts5i

Nonparametric Neural Network Model Based on Rough-Fuzzy Membership Function for Classification of Remotely Sensed Images [chapter]

Niraj Kumar, Anupam Agrawal
2006 Lecture Notes in Computer Science  
A nonparametric neural network model based on Rough-Fuzzy Membership function, multilayer perceptron, and back-propagation algorithm is described.  ...  The input vector consists of membership values to linguistic properties while the output vector is defined in terms of rough fuzzy class membership values.  ...  Here we have used the generalized concept of rough membership function in pattern classification tasks to Rough-Fuzzy Membership functions to deal with rough uncertainty [9] in geospatial information  ... 
doi:10.1007/11949619_10 fatcat:znci4wb3jjfxzom4rtqmzr3orm

A Deep Convolutional Neural Network with fuzzy rough sets for FER

Xiangjian Chen, Di Li, Xibei Yang
2019 IEEE Access  
Based on the type 2 fuzzy rough sets theory, we construct an optimization objective for training CNNs by minimizing fuzzy classification uncertainty, and present the defifinition and optimization of type  ...  This method could reduce the uncertainty in terms of vagueness and indiscernibility by using type 2 fuzzy rough sets theory and specififically removing noise samples by using CNN from raw data.  ...  The type 2 fuzzy rough loss function is used as the loss function layer to train convolutional neural network in fuzzy expression classification problem.  ... 
doi:10.1109/access.2019.2960769 fatcat:es4zyhyuyrfxdbwlljxwbpgmha

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)  
To overcome this problem, this paper presents an efficient approach to predict the dominant genes using fuzzy-rough boundary region-based feature selection in combination with a heuristic hill-climber  ...  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.  ...  The fuzzy-rough set model may be used to reveal the knowledge hidden in fuzzy decision systems.  ... 
doi:10.1109/his.2013.6920499 dblp:conf/his/Dash13 fatcat:tsmuzwnkujemxaekwwj4pp2wtm

Fuzzy-rough nearest neighbor algorithms in classification

Manish Sarkar
2007 Fuzzy sets and systems (Print)  
In this paper, classification efficiency of the conventional K-nearest neighbor algorithm is enhanced by exploiting fuzzy-rough uncertainty.  ...  Moreover, the generated class confidence values, which are interpreted in terms of fuzzy-rough ownership values, do not necessarily sum up to one.  ...  function in the proposed algorithm, we show that the fuzzy-rough ownership function is able to quantify the fuzzy-roughness present in the data, and the quantified value lies in [0, 1].  ... 
doi:10.1016/j.fss.2007.04.023 fatcat:q4sl6g7bpfdfxf5san5ddirwba

Rough-fuzzy Classifier Modeling Using Data Repository Sets

Jiri Krupka, Pavel Jirava
2014 Procedia Computer Science  
It also deals with uncertainty in an information system and the two approaches, the fuzzy sets (FSs) and rough sets theory (RST), for operating it.  ...  Two components of the computational intelligence (CI) are applied, rough and fuzzy sets theory. These components permit one to operate with uncertainty data.  ...  In 27 a fuzzy-rough ownership function that involves the fuzzy uncertainty and the rough uncertainty is proposed.  ... 
doi:10.1016/j.procs.2014.08.152 fatcat:ysa4w5fg7jayjefn3fjaiwhkwm

Performance of Fuzzy Rough Sets and Fuzzy Evolutionary Classifiers using Medical Databases

This paper proposes a comparison of fuzzy hybrid techniques like Fuzzy Rough set and Fuzzy EA for the diagnosis of Hepatitis taken from UCI repository.  ...  The aim of this paper is to construct algorithms for extracting the relevant information from the large amount of data and classifying using various hybrid techniques like Fuzzy-Rough set and Fuzzy Evolutionary  ...  In this paper, we focus on hybridization of fuzzy with rough set and genetic algorithms and classification done. II.  ... 
doi:10.35940/ijitee.j1066.0881019 fatcat:ry2bdbgezrclpooz53avj25oei

Kernelized Fuzzy Rough Sets and Their Applications

Qinghua Hu, Daren Yu, Witold Pedrycz, Degang Chen
2011 IEEE Transactions on Knowledge and Data Engineering  
In this study, we integrate kernel functions with fuzzy rough set models and propose two types of kernelized fuzzy rough sets.  ...  Kernel functions are employed to compute the fuzzy T-equivalence relations between samples, thus generating fuzzy information granules in the approximation space.  ...  Thus, the relations computed with these functions are fuzzy T -equivalence relations. With the kernel function we can substitute fuzzy relations in fuzzy rough sets. Definition 2.  ... 
doi:10.1109/tkde.2010.260 fatcat:tfocwlycr5gife23qrqhmlwovy

Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications

Qinghua Hu, Lei Zhang, Degang Chen, Witold Pedrycz, Daren Yu
2010 International Journal of Approximate Reasoning  
Gaussian kernels are introduced to acquire fuzzy relations between samples described by fuzzy or numeric attributes in order to carry out fuzzy rough data analysis.  ...  Kernel methods and rough sets are two general pursuits in the domain of machine learning and intelligent systems.  ...  The features derived by Gaussian based fuzzy rough sets and fuzzy entropy get the higher classification accuracies in most of the datasets.  ... 
doi:10.1016/j.ijar.2010.01.004 fatcat:rsujs7jw4rbhbowhcal4tjji2y

An Exponential Kernel based Fuzzy Rough Sets Model for Feature Selection

Riaj UddinMazumder, Shahin Ara Begum, Devajyoti Biswas
2013 International Journal of Computer Applications  
Experimental results demonstrate the effectiveness of the Exponential kernel based fuzzy rough sets approach for feature selection in improving the classification accuracy in comparison to Gaussian kernel  ...  Fuzzy rough set plays a prominent role in dealing with imprecision and uncertainty. Some problem domains have motivated the hybridization of fuzzy rough sets with kernel methods.  ...  The kernel functions extract fuzzy relations from data into fuzzy rough sets.  ... 
doi:10.5120/14016-2155 fatcat:c5hyhyhkdvco3frkrqh26xig4a

Survey of Rough and Fuzzy Hybridization

Pawan Lingras, Richard Jensen
2007 IEEE International Fuzzy Systems conference proceedings  
Since both theories originated in the expert system domain, there are a number of research proposals that combine rough and fuzzy concepts in supervised learning.  ...  However, continuing developments of rough and fuzzy extensions to clustering, neurocomputing, and genetic algorithms make hybrid approaches in these areas a potentially rewarding research opportunity as  ...  The authors applied rough GAs based on lower and upper bounds of numeric ranges to the classification problem in an undetermined environment based on a fuzzy distance function by calculating attribute  ... 
doi:10.1109/fuzzy.2007.4295352 dblp:conf/fuzzIEEE/LingrasJ07 fatcat:vygojaq46zb6zej6q3jtlna5au

Biological image classification using rough-fuzzy artificial neural network

Carlos Affonso, Renato Jose Sassi, Ricardo Marques Barreiros
2015 Expert systems with applications  
This paper presents a methodology to biological image classification through a Rough-Fuzzy Artificial Neural Network (RFANN).  ...  This result could be achieved, once the image features are characterized using membership functions and reduced it by Fuzzy Sets rules.  ...  image classification (Shang & Barnes, 2013), Rough Sets and Near Sets in Medical Imaging (Hassanien, Abraham, Peters, Schaefer, & Henry, 2009), Implementation and comparative analysis of Rough Set, Artificial  ... 
doi:10.1016/j.eswa.2015.07.075 fatcat:vx7nydtenbeivhbz43zggifqgu

Fuzzy Rough Data Reduction Using SVD

Rama Devi Y, Venu Gopal P, Sai Prasad PSVS
2011 International Journal of Computer and Electrical Engineering  
This paper shows how singular value decomposition can be used as a useful preprocessing method in order to achieve fuzzy rough reduct convergence on higher dimensional datasets.  ...   Abstract-Fuzzy rough data reduction algorithm proposed in [1] is not convergent on higher dimensional data due to its computational complexity increases exponentially as the number of input attributes  ...  The reduced dataset found by fuzzy rough and ant fuzzy rough based on SVD were divided in to training and testing, and then the classification accuracies were taken w.r.t different classifiers.  ... 
doi:10.7763/ijcee.2011.v3.346 fatcat:3wjwhbygnnbateoopiqa5bc3ge

Evolutionary sequential genetic search technique-based cancer classification using fuzzy rough nearest neighbour classifier

Loganathan Meenachi, Srinivasan Ramakrishnan
2018 Healthcare technology letters  
In this Letter, the authors propose a genetic search fuzzy rough (GSFR) feature selection algorithm, which is hybridised using the evolutionary sequential genetic search technique and fuzzy rough set to  ...  The generated subset is subjected to the evaluation with the modified dependency function of the fuzzy rough set using positive and boundary regions, which act as a fitness function.  ...  This is due to the fact that the modified dependency function of the fuzzy rough set is adopted as a fitness function in the proposed GSFR feature selection algorithm.  ... 
doi:10.1049/htl.2018.5041 pmid:30155265 pmcid:PMC6103784 fatcat:suq64cg6izdxzaeaqxg33luwbq

Fuzzy - Rough Feature Selection With Π- Membership Function For Mammogram Classification [article]

K.Thangavel, R.Roselin
2012 arXiv   pre-print
In this paper, Fuzzy-Rough feature selection with π membership function is proposed. The selected features are used to classify the abnormalities with help of Ant-Miner and Weka tools.  ...  In this study mammograms are preprocessed and features are extracted, then the abnormality is identified through the classification.  ...  Fuzzy-Rough-π Quickreduct Algorithm It is similar to the Fuzzy-Rough QuickReduct but the membership function is calculated using πmembership function with three linguistic fuzzy sets.  ... 
arXiv:1205.4336v2 fatcat:hxkda5ha7zeuloh3lg7sdxpfda
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