Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

2021 ◽  
Author(s):  
Wanting Ji ◽  
Yan Pang ◽  
Xiaoyun Jia ◽  
Zhongwei Wang ◽  
Feng Hou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Rough Sets ◽  
Fuzzy Rough Sets

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

2013 ◽  
Vol 48 ◽  
pp. 91-108 ◽  
Author(s):  
Avatharam Ganivada ◽  
Shubhra Sankar Ray ◽  
Sankar K. Pal
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Rough Sets ◽  
Fuzzy Rough Sets ◽  
Unsupervised Feature Selection

Feature selection for multi-label learning based on kernelized fuzzy rough sets

2018 ◽  
Vol 318 ◽  
pp. 271-286 ◽  
Author(s):  
Yuwen Li ◽  
Yaojin Lin ◽  
Jinghua Liu ◽  
Wei Weng ◽  
Zhenkun Shi ◽  
...  
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Fuzzy Rough Sets ◽  
Selection For

Using Rough Sets for Practical Feature Selection in a Rough Sets/Neural Network Framework for Knowledge Discovery

2000 ◽  
Vol 4 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Ilona Jagielska ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Selection ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Real World ◽  
Rough Sets ◽  
Network Models ◽  
Feature Subset Selection ◽  
Neural Network Models

An important task in knowledge discovery is feature selection. This paper describes a practical approach to feature subset selection proposed as part of a hybrid rough sets/neural network framework for knowledge discovery for decision support. In this framework neural networks and rough sets are combined and used cooperatively during the system life cycle. The reason for combining rough sets with neural networks in the proposed framework is twofold. Firstly, rough sets based systems provide domain knowledge expressed in the form of If-then rules as well as tools for data analysis. Secondly, rough sets are used in this framework in the task of feature selection for neural network models. This paper examines the feature selection aspect of the framework. An empirical study that tested the approach on artificial datasets and real-world datasets was carried out. Experimental results indicate that the proposed approach can improve the performance of neural network models. The framework was also applied in the development of a real-world decision support system. The experience with this application has shown that the approach can support the users in the task of feature selection.

scholarly journals An Exponential Kernel based Fuzzy Rough Sets Model for Feature Selection

2013 ◽  
Vol 81 (6) ◽  
pp. 24-31
Author(s):  
Riaj UddinMazumder ◽  
Shahin Ara Begum ◽  
Devajyoti Biswas
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Fuzzy Rough Sets ◽  
Exponential Kernel

scholarly journals Feature Selection Algorithm using Fuzzy Rough Sets for Predicting Cervical Cancer Risks

2010 ◽  
Vol 4 (8) ◽  
Author(s):  
Vandar Kuzhali Jagannathan ◽  
Rajendran Govind ◽  
Srinivasan V ◽  
Siva Kumar Ganapathi
Keyword(s):  
Cervical Cancer ◽  
Feature Selection ◽  
Rough Sets ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Cancer Risks ◽  
Fuzzy Rough Sets

Distributed Feature Selection for Big Data Using Fuzzy Rough Sets

2020 ◽  
Vol 28 (5) ◽  
pp. 846-857 ◽  
Author(s):  
Linghe Kong ◽  
Wenhao Qu ◽  
Jiadi Yu ◽  
Hua Zuo ◽  
Guihai Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Rough Sets ◽  
Fuzzy Rough Sets ◽  
Selection For
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