Paper Info
Title
Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets".
Abstract
The package RoughSets, written mainly in the R language, provides implementations of methods from the rough set theory (RST) and fuzzy rough set theory (FRST) for data modeling and analysis. It considers not only fundamental concepts (e.g., indiscernibility relations, lower/upper approximations, etc.), but also their applications in many tasks: discretization, feature selection, instance selection, rule induction, and nearest neighbor-based classifiers. The package architecture and examples are presented in order to introduce it to researchers and practitioners. Researchers can build new models by defining custom functions as parameters, and practitioners are able to perform analysis and prediction of their data using available algorithms. Additionally, we provide a review and comparison of well-known software packages. Overall, our package should be considered as an alternative software library for analyzing data based on RST and FRST.
Year
DOI
Venue
2014
10.1016/j.ins.2014.07.029
Information Sciences
Keywords
Field
DocType
Rough set,Fuzzy rough set,Instance selection,Discretization,Feature selection,Rule induction
Discretization,Data mining,Data modeling,Feature selection,Computer science,Software,Artificial intelligence,Dominance-based rough set approach,k-nearest neighbors algorithm,Algorithm,Rough set,Rule induction,Machine learning
Journal
Volume
ISSN
Citations 
287
0020-0255
36
PageRank 
References 
Authors
1.43
66
7
Name
Order
Citations
PageRank
Lala Septem Riza1361.43
Andrzej Janusz225224.21
Christoph Bergmeir315214.04
Chris Cornelis42116113.39
Francisco Herrera5273911168.49
Dominik Şlȩzak672950.65
José Manuel Benítez788856.02