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Fuzzy rough sets, and a granular neural network for unsupervised feature selection

Avatharam Ganivada, Shubhra Sankar Ray, Sankar K. Pal
2013 Neural Networks  
A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed.  ...  The input vector and the target value of the network are defined using granulation structures, based on the concept of fuzzy sets.  ...  granules generated by fuzzy rough sets are incorporated into artificial neural networks.  ... 
doi:10.1016/j.neunet.2013.07.008 pmid:23994187 fatcat:zmg5muq4ffb5roiw5322mi7kuy

Prediction of Student Performance Using Rough Set Theory And Backpropagation Neural Networks

Bruno Cristos Madeira, Tugrul Tasci, Numan Celebi
2021 European Scientific Journal  
In this paper, we evaluate the usefulness of a model using Rough Set Theory (RST) and Backpropagation Neural Network (BPNN) in effectively predicting the students' overall performance.  ...  Thus outperforming a model based solely on BPNN used on the original dataset and reducing computational costs.  ...  Conclusion In this study the end of course performance of students in a language institute was predicted using Rough Set Theory (RST) and Backpropagation Neural Network (BPNN).  ... 
doi:10.19044/esj.2021.v17n7p1 fatcat:wnvaj5qn75el7ckchep7bh5lom

Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks [chapter]

Kenneth Revett, Marcin Szczuka, Pari Jahankhani, Vassilis Kodogiannis
2006 Lecture Notes in Computer Science  
This paper describes the application of rough sets and neural network models for classification of electroencephalogram (EEG) signals from two patient classes: normal and epileptic.  ...  These results were confirmed using standard neural network based classifiers.  ...  Classification Methods In this study, we employed a combination of neural network based classification algorithms in conjunction with rough sets.  ... 
doi:10.1007/11908029_43 fatcat:avcrkjzzonaktodkh3y2qebnmu

Application of Rough Set and Neural Network in Water Energy Utilization

Minghua Wei, Zhihong Zheng, Zhihong Zheng, Xiao Bai, Ji Lin, Farhad Taghizadeh-Hesary
2021 Frontiers in Energy Research  
This work combines a rough set and artificial neural network and uses it in fault diagnosis of hydraulic turbine conversion, puts forward rough set theory based on the tolerance relation and defines similarity  ...  The diagnostic rate of artificial neural networks based on a rough set is higher than that of the general three-layer back-propagation(BP) neural network, and the training time is also shortened.  ...  One model is the rough neural network, using a rough set to handle front-end data of neural network input and using rough set mining rules to replace the conventional adaptive-network-based fuzzy inference  ... 
doi:10.3389/fenrg.2021.604660 doaj:1655160bb85f4d80a435fa948e45b36b fatcat:pkuzb5hmtzfknhtxc24k47zol4

Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

Kindie Biredagn Nahato, Khanna Nehemiah Harichandran, Kannan Arputharaj
2015 Computational and Mathematical Methods in Medicine  
The second stage is classification using backpropagation neural network on the selected reducts of the dataset.  ...  In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages.  ...  Table 14 compares Conclusion and Future Work This paper combines rough set theory with backpropagation neural network to classify the clinical dataset.  ... 
doi:10.1155/2015/460189 pmid:25821508 pmcid:PMC4364360 fatcat:awr4jjju7nekblrpua7d34xnwu

The prediction of virus mutation using neural networks and rough set techniques

Mostafa A. Salama, Aboul Ella Hassanien, Ahmad Mostafa
2016 EURASIP Journal on Bioinformatics and Systems Biology  
Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns.  ...  This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques.  ...  A special thanks for Mr. Saleh Esmate for helping us in gathering the data set of this work.  ... 
doi:10.1186/s13637-016-0042-0 pmid:27257410 pmcid:PMC4867776 fatcat:64tawamgozdgrdenmfoiprydbu

Prediction of Breast Cancer through Tolerance-based Intuitionistic Fuzzy-rough Set Feature Selection and Artificial Neural Network

Ercan CELIK, Naiyer Mohammadi LANBARAN
Highlights • This paper focuses on feature selection process by tolerance-based intuitionistic fuzzy-rough set. • Two hybrid approaches are proposed for classification in the study. • A highly precise  ...  This study aims to use the tolerance-based intuitionistic fuzzy-rough set approach to pick attributes and data processing with help of machine learning for the classification of breast cancer.  ...  The preferable accuracy of 89% was obtained by the Artificial Neural Network method for the better classification of a data stream.  ... 
doi:10.35378/gujs.857099 fatcat:hq5fbrnayfdabo4evyol2gc4ce

Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

Xiaoqing Li, Qingquan Jiang, Maxwell K. Hsu, Qinglan Chen
2019 Sustainability  
In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set  ...  First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list.  ...  The test results indicated that, compared with the single BP neural network model, the classification and prediction accuracy of the proposed model based on the combination of the rough set and BP neural  ... 
doi:10.3390/su11174513 fatcat:oo3rlr6z6jcb3i2al5yby73eie

A Novel Classification Approach through Integration of Rough Sets and Back-Propagation Neural Network

Lei Si, Xin-hua Liu, Chao Tan, Zhong-bin Wang
2014 Journal of Applied Mathematics  
Classification is an important theme in data mining. Rough sets and neural networks are the most common techniques applied in data mining problems.  ...  In order to extract useful knowledge and classify ambiguous patterns effectively, this paper presented a hybrid algorithm based on the integration of rough sets and BP neural network to construct a novel  ...  In this paper, a novel classification system based on the integration of rough sets and BP neural network is proposed.  ... 
doi:10.1155/2014/797432 fatcat:dfuvmhovprgbnonhettds6pmym

Novel Approach of Fault Diagnosis in Wireless Sensor Networks Node Based On Rough Set and Neural Network Model

Hongsheng Xu, Ruiling Zhang, Chunjie Lin, Youzhong Ma
2016 International Journal of Future Generation Communication and Networking  
This paper adopts attribute reduction algorithm by integrate rough set with neural network model to eliminate WSN node failure, so as to achieve data reduction and to improve the accuracy and efficiency  ...  Rough set can deal with incomplete information, especially in the data reduction, and it is easy to realize low energy consumption problem of on-line fault diagnosis based on WSN node energy Co.  ...  Department (13B520155) and Henan Province basic and frontier technology research project (142300410303).  ... 
doi:10.14257/ijfgcn.2016.9.4.01 fatcat:q425nmyddfbsda4rjlyoyc543y

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  
Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability.  ...  Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information.  ...  research work is limited to the feature selection, in future it can be extended by using hybrid technique for both instance and feature selection.  ... 
doi:10.54692/lgurjcsit.2021.0503224 fatcat:jcweew3hzbfelnwc2vncjucqr4


Ambily Merlin Kuruvilla, Dr. Balaji N.V.
2021 Indian Journal of Computer Science and Engineering  
A novel meta-heuristic soft computing model with feature selection implemented using Rough set (RS) theory for the diagnosis of coronary artery disease in diabetes patients is proposed in this study.  ...  The binary classification method in multiclass classification problems is applied by the One Versus Rest approach (OVR) is incorporated.  ...  In this study, a novel soft computing model is proposed for heart disease prediction among diabetes patients based on Rough set-based feature selection with an Artificial Neural Network optimized using  ... 
doi:10.21817/indjcse/2021/v12i4/211204161 fatcat:jslugzvbm5f4xmixchohazgtre

Intrusion Detection System Based on Genetic Attribute Reduction Algorithm Based on Rough Set and Neural Network

Jan Luo, Huajun Wang, Yanmei Li, Yuxi Lin, Kalidoss Rajakani
2022 Wireless Communications and Mobile Computing  
Therefore, this paper proposes an intrusion detection system based on the combination of genetic attribute reduction algorithm based on rough set and neural network.  ...  Based on the traditional BP neural network, this paper combines the genetic attribute reduction algorithm based on rough set to optimize the structure and performance of the system.  ...  On the basis of this research result, other literatures compare the accuracy and effectiveness of real-time intrusion detection combined with neural network and further study the research on the sorting  ... 
doi:10.1155/2022/5031236 fatcat:b5rlsepjfvdlzlye5x5eqojvki

Fault Diagnosis for Wireless Sensor Network's Node Based on Hamming Neural Network and Rough Set

Lei Lin, Hou-jun Wang, Chuan-long Dai
2008 2008 IEEE Conference on Robotics, Automation and Mechatronics  
Finally, a set of method for fault classification was founded by hamming network.  ...  Furthermore, a set of model for node's fault diagnosis in WSN could be built through classification algorithm based on attribute matching.  ...  Crash faults identification in WSN is studied in literature [1] . In this paper, based on Rough set theory, a novel method of fault diagnosis for node in WSN was brought forward.  ... 
doi:10.1109/ramech.2008.4681504 dblp:conf/ram/LeiWD08 fatcat:jtutzixozbesrnohtmpgsdavcq

A rule based approach to classification of EEG datasets: A comparison between ANFIS and rough sets

Pari Jahankhani, Kenneth Revett, Vassilis Kodogiannis
2008 2008 9th Symposium on Neural Network Applications in Electrical Engineering  
We compared the application of a neuro-fuzzy model (ANFIS) and rough sets, a by now classical rule-based classifier.  ...  To compare the classification results of both ANFIS and rough sets, the attribute selected by rough sets (MaxD4) and the first principal component were used as the input into the ANFIS system. where MaxD4  ... 
doi:10.1109/neurel.2008.4685599 fatcat:zt4rco6wbjfijbrvdexiro7fdq
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